Jobs Kyiv
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Β· 121 views Β· 6 applications Β· 2d
Junior Data Scientist/Quant Researcher to $2500
Office Work Β· Ukraine (Kyiv) Β· Product Β· 2 years of experience Β· Upper-IntermediateAbout Us: Atto Trading is a quantitative trading firm operating a portfolio of signal-driven high-frequency strategies in cash equities and futures. We are building a global, diverse team, with experts in trading, statistics, engineering, and technology...About Us:
Atto Trading is a quantitative trading firm operating a portfolio of signal-driven high-frequency strategies in cash equities and futures.
We are building a global, diverse team, with experts in trading, statistics, engineering, and technology to trade global markets. Our disciplined approach combined with rapid market feedback allows us to quickly turn ideas into profit. Our environment of learning & collaboration allows us to solve the worldβs hardest problems, together.
As a small firm, we remain nimble and hold ourselves to the highest standards of integrity, ingenuity, and effort.
About the Role:
We're looking for a Junior Data Scientist/Quant Researcher to join our profitable trading team and drive growth.
This position is currently open as remote work from Ukraine, with in-office presence in Kyiv required once circumstances allow.
At ATTO Trading, you'll build models, strategies, and systems for trading various financial instruments globally. This role blends trading and software development, involving data analysis, predictive modeling, and strategy development. You'll tackle some of the industry's toughest challenges and work with cutting-edge technology.
Responsibilities:
- Design and test new data pipelines (ETL/ELT)
- Develop data visualization tools
- Maintain existing analytical instruments (toolkits, pipelines)
- Participate in research projects
Requirements:
- High level of proficiency in Python programming (data structures, design patterns, OOP, PEP etc)
- Experience with analytical packages for data visualization, manipulation and processing: NumPy, Polars, Matplotlib, Bokeh, Scikit-Learn
- Basic knowledge of relational databases and SQL
- Upper-Intermediate+ in English
- Experience working in Linux environment
- Bachelorβs degree in statistics, math, computer science, or another quantitative discipline
- Good communication and team skills
- Close attention to details
Nice to have:
- Experience with Π‘++
- Understanding of basic machine learning techniques
- Genuine interest in finance and trading
- Experience in development of quantitative trading strategies
Benefits:
- Opportunity to develop professional competencies
- Interesting and challenging tasks
- Competitive rates of pay
- Paid time off
- Coverage of health insurance cost
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Β· 49 views Β· 1 application Β· 20d
Computer Vision Engineer
Office Work Β· Ukraine (Kyiv) Β· Product Β· 5 years of experience Β· Intermediate MilTech πͺOverview We are seeking a highly skilled and experienced Senior/Lead Computer Vision Engineer specializing in Navigation to join our innovative R&D team. In this pivotal role, you will drive the development and deployment of state-of-the-art computer...Overview
We are seeking a highly skilled and experienced Senior/Lead Computer Vision Engineer specializing in Navigation to join our innovative R&D team. In this pivotal role, you will drive the development and deployment of state-of-the-art computer vision algorithms for autonomous navigation systems, contributing to our efforts in robotics, autonomous vehicles, drones, or similar fields. You will work cross-functionally with engineering, product, and research teams to deliver robust, real-time solutions that enable safe and intelligent navigation in dynamic environments.
Responsibilities- Lead the design, development, and optimization of computer vision algorithms for localization, mapping, and navigation.
- Develop and implement algorithms for object detection, segmentation, SLAM, 3D scene reconstruction, visual odometry, and sensor fusion (using cameras, LiDAR, IMUs, etc.).
- Guide the integration of computer vision modules with navigation and control systems, ensuring seamless operation in real-world conditions.
- Collaborate with software, hardware, and product teams to define requirements and deliver scalable, robust navigation solutions.
- Stay current with advancements in deep learning, computer vision, and robotics, and introduce relevant state-of-the-art techniques into the product.
- Design and execute experiments to evaluate performance and robustness; analyze results and iterate on solutions.
- Prepare technical documentation, progress reports, and presentations for internal and external stakeholders.
Requirements- 5+ years of experience in computer vision, preferably in navigation, robotics, or autonomous systems.
- Masterβs or PhD in Computer Science, Robotics, Electrical Engineering, or related field.
- Strong proficiency in Python and/or C++.
- Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow) and classical computer vision libraries (e.g., OpenCV, PCL).
- Experience in deploying and optimizing models for single-board computers such as Raspberry Pi, Nvidia Jetson
- Proven track record of developing and deploying real-time vision algorithms for navigation tasks in challenging environments.
- Extensive knowledge of SLAM, visual odometry, sensor fusion, and related algorithms.
- Experience with ROS, embedded systems, and real-time software development is a plus.
- Excellent problem-solving skills, strong analytical mindset, and effective communication abilities.
Preferred Qualifications- Knowledge of SLAM and related models.
- Familiarity with the MAVLink protocol and ArduPilot.
- Familiarity with edge computing or real-time GPU-based inference.
- Publications or contributions to the open-source community in vision or robotics.
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Β· 20 views Β· 0 applications Β· 6d
Computer Vision Engineer
Ukraine Β· Product Β· 4 years of experience MilTech πͺWe are looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms. The ideal candidate will have experience with SLAM, Visual-Inertial Odometry (VIO), and sensor...We are looking for a Computer Vision Engineer with a background in classical computer vision techniques and hands-on implementation of low-level CV algorithms.
The ideal candidate will have experience with SLAM, Visual-Inertial Odometry (VIO), and sensor fusion.
We consider engineers at Middle/Senior levels β tasks and responsibilities will be adjusted accordingly.
Required Qualifications:
- 3+ years of hands-on experience with classical computer vision
- Knowledge of popular computer vision networks and components
- Understanding of geometrical computer vision principles
- Hands-on experience in implementing low-level CV algorithms
- Practical experience with SLAM and/or Visual-Inertial Odometry (VIO)
- Proficiency in C++
- Experience with Linux
- Ability to quickly navigate through recent research and trends in computer vision.
Nice to Have:
- Experience with Python
- Familiarity with neural networks and common CV frameworks/libraries (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
- Experience with sensor fusion.
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Β· 368 views Β· 51 applications Β· 25d
Junior Data Scientist/Quant Researcher Internship
Part-time Β· Office Work Β· Ukraine (Kyiv, Lviv) Β· Product Β· Upper-IntermediateJunior Data Scientist/Quant Researcher Internship β Your Entry Point Into the World of High-Frequency Trading Are you passionate about data analysis? Do you thrive on competitions, love solving unconventional problems, and want to test yourself in an...Junior Data Scientist/Quant Researcher Internship β
Your Entry Point Into the World of High-Frequency TradingAre you passionate about data analysis? Do you thrive on competitions, love solving unconventional problems, and want to test yourself in an industry where every nanosecond counts?
Join us for a paid internship at Atto Trading β an American HFT firm that builds high-frequency trading algorithms for U.S. financial markets.
πΌ About Atto Trading β Where Engineering Meets Finance
Atto Trading is an American tech-driven trading company that builds and runs high-frequency, algorithmic strategies on financial markets.
Weβre a small, focused team of engineers, traders, and researchers who:
- π‘ Turn ideas into profitable strategies
- πΊπΈ Operate on U.S. financial markets
- π§ Constantly learn and tackle hard technical challenges together
- π Build ultra-fast, rock-solid software
As an intern, you wonβt be stuck on side tasks β youβll be a real part of the engineering team from day one.
π§ During the internship, you will do:
- Analyze large datasets of market data using Polars, SQLAlchemy, scikit-learn etc
- Build ipynb reports and interactive visualizations using Bokeh or Shiny-Plotly that provide real value
- Work with private analytical tools to create pipelines for subindicators generation
- Collaborate with engineers, traders, and analysts in a real-world business environment
π Program Details
- π Duration: 3 weeks, summer (JulyβAugust)
- π Location: Ukraine in-office (Lviv, Kyiv) or partly remote (final format to be confirmed)
- πΌ Format: Paid internship
- π One top-performing intern will be offered a full-time or part-time role (depending on academic commitments)
- π The selected intern will receive an iPad as part of the welcome package from Atto Trading
This is a unique opportunity to gain real-world experience in high-frequency trading (HFT) β where analytics, precision, and speed determine success.
Please note: This internship is intended for candidates located in Ukraine.
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Β· 35 views Β· 1 application Β· 6d
ML / Computer Vision Engineer (Human Understanding)
Ukraine Β· Product Β· 3 years of experience Β· IntermediateSamsung R&D Institute Ukraine (SRUKR) is looking for ML / Computer Vision engineer who wants to apply theoretical knowledge and practical skills to participate in solving Human Understanding challenges in rapidly evolving Vision AI domain. The position...Samsung R&D Institute Ukraine (SRUKR) is looking for ML / Computer Vision engineer who wants to apply theoretical knowledge and practical skills to participate in solving Human Understanding challenges in rapidly evolving Vision AI domain. The position will involve different aspects of R&D including β research, analysis, prototyping, development and commercialization support of the innovative technologies. Resulting solutions are targeted on Samsung products and services reaching millions of users worldwide.
Required skills / expertise:
- Bachelor's (or higher) degree in computer science, math, statistics, or related field
- 3+ years of experience in conventional and ML/DL based image processing and computer vision
- Practical experience in custom NN-architecture development, training and evaluation
- Strong theoretical knowledge and practical skills in computer vision algorithms (OpenCV)
- Solid Python programming skills (numpy, pandas, matplotlib)
- Knowledge in linear algebra, probability, optimization, and 3D geometry
- Proficiency in math, algorithms and data structures
- Experience with object-oriented design and development
- Basic C++ knowledge
- Understanding research methodologies and S/W development lifecycle
Would be a plus:
- Experience in 3D face reconstruction and face attributes detection
- Experience with ComfyUI and data generation activities
- Participation in CV/ML/DL-intensive research (papers, competitions, patents, etcβ¦)
- Pet projects portfolio that includes β object detection/recognition/tracking, key-points detection and tracking, semantic/instance segmentation, etc.
- Experience with vision transformer, vision encoder-decoder architectures
- Experience with model optimizations for on-device inference (ONNX-runtime, TFLite, SNPE)
- Experience with CPU/GPU profiling tools
- Cross-cultural experience and working English to feel confident in the international team
Key Responsibilities:
- R&D activities in CV based Human Understanding domain (person/face attributes detection, recognition and tracking, 3D face reconstruction).
- Design NN-based solutions and train required ML/DL models
- Optimize algorithms & ML/DL models / their inference and size
- Transfer models and solutions to the edge devices using appropriate frameworks (ONNX, TFLite, SNPE, etc)
- Participate in design process of system architecture
- Collaborate with other R&D engineers worldwide to improve product quality with the latest industry trends in relevant technologies
- Maintain and support existing solutions and services
- Develop demo applications for various platforms
- Opportunity to participate in publication and patent activities
Working Conditions:
- GIG contract
- remote work is possible as well as work in Kyiv office
Benefits:
- competitive salary, annual salary review, annual bonuses
- paid 28 work days of annual vacations and sick leaves
- opportunity to become an inventor of international patents with paid bonuses
- medical & life insurance for employees and their childrens
- paid lunches
- discounts to Samsung products, services
- regular education and self-development on internal courses and seminars
- hybrid work format, working in office is required for some tasks
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Β· 26 views Β· 1 application Β· 5d
Senior Data Scientist (AI)
Ukraine Β· Product Β· 5 years of experience Ukrainian Product πΊπ¦Π ΠΊΠΎΠΌΠ°Π½Π΄Ρ DataDiscovery ΡΡΠΊΠ°ΡΠΌΠΎ Π½Π° ΡΠΎΠ·ΡΠΈΡΠ΅Π½Π½Ρ Senior Data Scientist. ΠΠ°Ρ ΡΠ΄Π΅Π°Π»ΡΠ½ΠΈΠΉ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ ΠΌΠ°Ρ: - 5+ ΡΠΎΠΊΠΈ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ. ΠΠ°Π²ΠΈΡΠΊΠΈ: - Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: TensorFlow, PyTorch; - Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ Big Data: Kafka, Amazon...Π ΠΊΠΎΠΌΠ°Π½Π΄Ρ DataDiscovery ΡΡΠΊΠ°ΡΠΌΠΎ Π½Π° ΡΠΎΠ·ΡΠΈΡΠ΅Π½Π½Ρ Senior Data Scientist.
ΠΠ°Ρ ΡΠ΄Π΅Π°Π»ΡΠ½ΠΈΠΉ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°Ρ ΠΌΠ°Ρ:
- 5+ ΡΠΎΠΊΠΈ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ.
ΠΠ°Π²ΠΈΡΠΊΠΈ:
- Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: TensorFlow, PyTorch;
- Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ Big Data: Kafka, Amazon S3, Spark;
- SQL ΡΠ° Π°Π½Π°Π»ΡΠ· Π΄Π°Π½ΠΈΡ : ΡΠΎΠ±ΠΎΡΠ° Π· Π±ΡΠ΄Ρ-ΡΠΊΠΈΠΌΠΈ Π΄ΠΆΠ΅ΡΠ΅Π»Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ (SQL, noSQL, Π²Π΅ΠΊΡΠΎΡΠ½Ρ Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , column-oriented Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , ΡΠΎΡΠΎ);
- Π₯ΠΌΠ°ΡΠ½Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ: AWS, GCP;
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π° ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ°: ΡΠ΅Π³ΡΠ΅ΡΡΡ, ΡΠΎΠ·ΠΏΠΎΠ΄ΡΠ» ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ, - ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠ° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ Π³ΡΠΏΠΎΡΠ΅Π· ΡΠΎΡΠΎ;
- ΠΡΠ΄Ρ ΠΎΠ΄ΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: ΡΠ΅Π³ΡΠ΅ΡΡΡ, ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΡΡ, Π΄Π΅ΡΠ΅Π²Π° ΡΡΡΠ΅Π½Ρ ΡΠ° ΡΠ½ΡΡ;
- ΠΠ»Π³ΠΎΡΠΈΡΠΌΠΈ Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: transformers, reinforcement learning, autoencoders, diffusion models, ΡΠΎΡΠΎ;
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² AI: NLP, CV, Recsys, Generative AI;
- MLOps.
Π©ΠΎ ΠΏΠΎΡΡΡΠ±Π½ΠΎ ΡΠΎΠ±ΠΈΡΠΈ:
- ΠΠΈΡΡΡΡΠ²Π°ΡΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²Ρ ΡΠ° Π΄ΠΎΡΠ»ΡΠ΄Π½ΠΈΡΡΠΊΡ Π²ΠΈΠΊΠ»ΠΈΠΊΠΈ ΠΊΡΡΡΠΎΠ³ΠΎ ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ΄ΡΠΊΡΡ;
- ΠΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΡΠ΅Π°Π»ΡΠ½ΠΈΡ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ²;
- ΠΠΈΠ²ΡΠ°ΡΠΈ ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΡΠ²Π°ΡΠΈ ΡΠΊΠ»Π°Π΄Π½Ρ state-of-the-art Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈ Π² ΠΎΠ±Π»Π°ΡΡΡ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ Π΄Π»Ρ Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΡ Π·Π°Π΄Π°Ρ;
- ΠΡΡΠ½ΡΠ²Π°ΡΠΈ ΡΠ΅Ρ Π½ΡΡΠ½Ρ ΠΊΠΎΠΌΠΏΡΠΎΠΌΡΡΠΈ ΠΏΠΎ ΠΊΠΎΠΆΠ½ΠΎΠΌΡ ΡΡΡΠ΅Π½Π½Ρ;
- ΠΡΠ°ΡΡΠ²Π°ΡΠΈ Π² ΡΡΡΠ½ΠΎΠΌΡ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΡΡΠ²Ρ Π· ΡΠ½ΡΠΈΠΌΠΈ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌΠΈ Π΄Π»Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΡΠ² AI.
Π©ΠΎ ΠΌΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- Π ΠΎΠ±ΠΎΡΡ Π² ΡΡΠ°Π±ΡΠ»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ β Π°Π΄ΠΆΠ΅ ΠΌΠΈ ΠΏΠΎΠ½Π°Π΄ 10 ΡΠΎΠΊΡΠ² Π½Π° ΡΠΈΠ½ΠΊΡ;
- ΠΡΠΉΡΠ½ΠΎ ΡΡΠΊΠ°Π²Ρ Π·Π°Π²Π΄Π°Π½Π½Ρ: Π±Π΅ΡΠΈ ΡΡΠ°ΡΡΡ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠ΅Π΄ΡΠ°ΡΠ΅ΡΠ²ΡΡΡ ΠΌΠ°ΠΉΠ±ΡΡΠ½ΡΠΎΠ³ΠΎ;
- ΠΡΠ΄Π½ΠΎΡΠΈΠ½ΠΈ, ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π°Π½Ρ Π½Π° Π΄ΠΎΠ²ΡΡΡ;
- ΠΠ°Π³Π°ΡΠΎ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π΄Π»Ρ ΡΠΎΠ·Π²ΠΈΡΠΊΡ;
- ΠΠ΅ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎ ΠΊΡΡΡΡ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²ΠΈ;
- ΠΠ΅Π·ΠΊΠΎΡΡΠΎΠ²Π½Ρ ΡΡΠΎΠΊΠΈ Π°Π½Π³Π»ΡΠΉΡΡΠΊΠΎΡ ΠΌΠΎΠ²ΠΈ;
- ΠΠ°Π½ΡΡΡΡ Π· ΠΏΠ»Π°Π²Π°Π½Π½Ρ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΡΠΎΠΊΠΈ Π½Π°ΡΡΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π½ΡΡΡ;
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³Π°;
- ΠΠ»Ρ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π·Π½ΠΈΠΆΠΊΠΈ Π²ΡΠ΄ Π±ΡΠ΅Π½Π΄ΡΠ² ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ².
ΠΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΠΈ Π½Π° Π²Π°ΠΊΠ°Π½ΡΡΡ Ρ Π½Π°Π΄ΡΡΠ»Π°Π²ΡΠΈ ΡΠ²ΠΎΡ ΡΠ΅Π·ΡΠΌΠ΅ Π² ΠΠΎΠΌΠΏΠ°Π½ΡΡ (Π’ΠΠ Β«ΠΠΠΠΠΠΒ»), Π·Π°ΡΠ΅ΡΡΡΡΠΎΠ²Π°Π½Ρ ΠΉ Π΄ΡΡΡΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°Π²ΡΡΠ²Π° Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΡΠ΅ΡΡΡΡΠ°ΡΡΠΉΠ½ΠΈΠΉ Π½ΠΎΠΌΠ΅Ρ 38347009, Π°Π΄ΡΠ΅ΡΠ°: Π£ΠΊΡΠ°ΡΠ½Π°, 01011, ΠΌΡΡΡΠΎ ΠΠΈΡΠ², Π²ΡΠ».Π ΠΈΠ±Π°Π»ΡΡΡΠΊΠ°, Π±ΡΠ΄ΠΈΠ½ΠΎΠΊ 22 (Π΄Π°Π»Ρ Β«ΠΠΎΠΌΠΏΠ°Π½ΡΡΒ»), Π²ΠΈ ΠΏΡΠ΄ΡΠ²Π΅ΡΠ΄ΠΆΡΡΡΠ΅ ΡΠ° ΠΏΠΎΠ³ΠΎΠ΄ΠΆΡΡΡΠ΅ΡΡ Π· ΡΠΈΠΌ, ΡΠΎ ΠΠΎΠΌΠΏΠ°Π½ΡΡ ΠΎΠ±ΡΠΎΠ±Π»ΡΡ Π²Π°ΡΡ ΠΎΡΠΎΠ±ΠΈΡΡΡ Π΄Π°Π½Ρ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ Ρ Π²Π°ΡΠΎΠΌΡ ΡΠ΅Π·ΡΠΌΠ΅, Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ ΠΠ°ΠΊΠΎΠ½Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ Β«ΠΡΠΎ Π·Π°Ρ ΠΈΡΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ Β» ΡΠ° ΠΏΡΠ°Π²ΠΈΠ» GDPR.
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Β· 131 views Β· 9 applications Β· 27d
Data Scientist
Ukraine Β· Product Β· 1 year of experience Β· Pre-IntermediateΠ ΠΊΠΎΠΌΠ°Π½Π΄Ρ 10 Π΄Π°ΡΠ° ΡΠ°ΡΠ½ΡΠΈΡΡΡΠ². ΠΡΠ°ΡΡΡΡΡ Π· ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠΎΡΠΈΠ½Π³Ρ, ΠΎΡΡΠ½ΠΊΠΈ ΡΠΈΠ·ΠΈΠΊΡ Π»ΡΠΊΠ²ΡΠ΄Π½ΠΎΡΡΡ, ΠΊΠΎΠ»Π΅ΠΊΡΠ½Ρ, Π°Π½ΡΠΈΡΡΠΎΠ΄Ρ, ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ. ΠΡΠ½ΠΎΠ²Π½Ρ Π²ΠΈΠΌΠΎΠ³ΠΈ ΠΠΠΠ'Π―ΠΠΠΠΠ 1+ ΡΡΠΊ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ Π½Π° Π°Π½Π°Π»ΠΎΠ³ΡΡΠ½ΡΠΉ ΠΏΠΎΡΠ°Π΄Ρ ΠΠ°ΠΊΡΠ½ΡΠ΅Π½Π° Π²ΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° (ΡΡΠ·ΠΈΠΊΠΎ-ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π°,...Π ΠΊΠΎΠΌΠ°Π½Π΄Ρ 10 Π΄Π°ΡΠ° ΡΠ°ΡΠ½ΡΠΈΡΡΡΠ². ΠΡΠ°ΡΡΡΡΡ Π· ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ ΠΊΡΠ΅Π΄ΠΈΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠΎΡΠΈΠ½Π³Ρ, ΠΎΡΡΠ½ΠΊΠΈ ΡΠΈΠ·ΠΈΠΊΡ Π»ΡΠΊΠ²ΡΠ΄Π½ΠΎΡΡΡ, ΠΊΠΎΠ»Π΅ΠΊΡΠ½Ρ, Π°Π½ΡΠΈΡΡΠΎΠ΄Ρ, ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ.
ΠΡΠ½ΠΎΠ²Π½Ρ Π²ΠΈΠΌΠΎΠ³ΠΈ
- ΠΠΠΠ'Π―ΠΠΠΠΠ 1+ ΡΡΠΊ ΠΊΠΎΠΌΠ΅ΡΡΡΠΉΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ Π½Π° Π°Π½Π°Π»ΠΎΠ³ΡΡΠ½ΡΠΉ ΠΏΠΎΡΠ°Π΄Ρ
- ΠΠ°ΠΊΡΠ½ΡΠ΅Π½Π° Π²ΠΈΡΠ° ΠΎΡΠ²ΡΡΠ° (ΡΡΠ·ΠΈΠΊΠΎ-ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ½Π°, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠ°, ΠΊΠΎΠΌΠΏ'ΡΡΠ΅ΡΠ½Ρ Π½Π°ΡΠΊΠΈ)
- Π‘ΠΊΡΡΠΏΡΠ»ΡΠΎΠ·Π½ΡΡΡΡ, ΡΠ²Π°ΠΆΠ½ΡΡΡΡ Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°Π»ΡΠ½ΡΡΡΡ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ·ΡΠΎΠ±ΠΊΠΈ, Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΠ° ΡΡΠΏΡΠΎΠ²ΠΎΠ΄ΠΆΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ
- ΠΠΎΡΠ²ΡΠ΄ Π½Π°ΠΏΠΈΡΠ°Π½Π½Ρ ΠΊΠ»Π°ΡΡΠ², ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ²
- ΠΠ½Π°Π½Π½Ρ Python
- ΠΠ°ΡΠ½Π΅ Π·Π½Π°Π½Π½Ρ SQL (Π°Π½Π°Π»ΡΠ·ΡΡΠΌΠΎ Π½Π°ΠΉΠ±ΡΠ»ΡΡΡ Π· ΡΡΡΡ Π±Π°Π½ΠΊΡΠ² Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ )
- ΠΠ½Π°Π½Π½Ρ ΠΎΡΠ½ΠΎΠ²Π½ΠΈΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² ML (ΠΊΠ»Π°ΡΠΈΡΠ½ΠΈΠΉ ML, ΡΠ΅Π³ΡΠ΅ΡΡΡ, ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ, ΠΏΡΠΎΠ³Π½ΠΎΠ· ΡΠ°ΡΠΎΠ²ΠΈΡ ΡΡΠ΄ΡΠ²)
ΠΡΠ΄Π΅ ΠΏΠ»ΡΡΠΎΠΌ
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Ρ ΡΡΠ½ΡΠ΅Ρ Π΄ΠΎΠΌΠ΅Π½Ρ
- ΠΠΎΡΠ²ΡΠ΄ Π· Amazon Sagemaker, Amazon S3
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Git
ΠΡΠ½ΠΎΠ²Π½Ρ ΠΎΠ±ΠΎΠ²βΡΠ·ΠΊΠΈ
- ΠΠ½Π°Π»ΡΠ· ΡΠ° Π²Π°Π»ΡΠ΄Π°ΡΡΡ Π΄Π°Π½ΠΈΡ
- Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΎΡΡΠ½ΠΊΠΈ ΡΠΈΠ·ΠΈΠΊΡΠ², ΡΠΊΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΡ ΠΊΡΠ°ΡΠΈΠΌ ΡΠ²ΡΡΠΎΠ²ΠΈΠΌ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ°ΠΌ
- ΠΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ, Π·Π°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎΡ ΡΠΊΠΎΡΡΡ
Π‘Π²ΠΎΡΠΌ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΠ°ΠΌ ΠΌΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ
- Π ΠΎΠ±ΠΎΡΡ Π² Π½Π°ΠΉΠ±ΡΠ»ΡΡΠΎΠΌΡ ΡΠ° ΡΠ½Π½ΠΎΠ²Π°ΡΡΠΉΠ½ΠΎΠΌΡ Π±Π°Π½ΠΊΡ Π£ΠΊΡΠ°ΡΠ½ΠΈ
- ΠΡΡΡΡΠΉΠ½Π΅ ΠΏΡΠ°ΡΠ΅Π²Π»Π°ΡΡΡΠ²Π°Π½Π½Ρ ΡΠ° 24 ΠΊΠ°Π»Π΅Π½Π΄Π°ΡΠ½ΠΈΡ Π΄Π½Ρ Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ
- ΠΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ Π»ΡΠΊΠ°ΡΠ½ΡΠ½ΠΈΡ
- ΠΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½Ρ Π·Π°ΡΠΎΠ±ΡΡΠ½Ρ ΠΏΠ»Π°ΡΡ
- ΠΠΎΠ½ΡΡΠΈ, ΠΏΡΠ΅ΠΌΡΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ ΠΏΠΎΠ»ΡΡΠΈΠΊΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ
- ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Π΅ Π½Π°Π²ΡΠ°Π½Π½Ρ
- ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π²ΡΠ΄Π΄Π°Π»Π΅Π½ΠΎΠ³ΠΎ ΡΠΎΡΠΌΠ°ΡΡ ΡΠΎΠ±ΠΎΡΠΈ
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΡΡΠ½Π°Π½ΡΠΎΠ²Ρ Π΄ΠΎΠΏΠΎΠΌΠΎΠ³Ρ Ρ ΠΊΡΠΈΡΠΈΡΠ½ΠΈΡ ΡΠΈΡΡΠ°ΡΡΡΡ
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Β· 156 views Β· 16 applications Β· 27d
Junior / Middle Data Scientist
Ukraine Β· Product Β· 1 year of experience Ukrainian Product πΊπ¦SKELAR β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ venture builder, ΡΠΊΠΈΠΉ Π±ΡΠ΄ΡΡ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ tech-Π±ΡΠ·Π½Π΅ΡΠΈ. Π Π°Π·ΠΎΠΌ ΡΠ· ΠΊΠΎ-ΡΠ°ΡΠ½Π΄Π΅ΡΠ°ΠΌΠΈ Π·Π±ΠΈΡΠ°ΡΠΌΠΎ ΡΠΈΠ»ΡΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎΠ± ΠΏΠ΅ΡΠ΅ΠΌΠ°Π³Π°ΡΠΈ Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΈΡ ΡΠΈΠ½ΠΊΠ°Ρ . Π‘ΡΠΎΠ³ΠΎΠ΄Π½Ρ Π² SKELAR β Π΄Π΅ΡΡΡΠΎΠΊ Π±ΡΠ·Π½Π΅ΡΡΠ² Ρ ΡΡΠ·Π½ΠΈΡ Π½ΡΡΠ°Ρ Π²ΡΠ΄ EdTech Π΄ΠΎ SaaS. Π¦Π΅ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎ ΠΌΠ°ΡΡΡ...SKELAR β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠΈΠΉ venture builder, ΡΠΊΠΈΠΉ Π±ΡΠ΄ΡΡ ΠΌΡΠΆΠ½Π°ΡΠΎΠ΄Π½Ρ tech-Π±ΡΠ·Π½Π΅ΡΠΈ. Π Π°Π·ΠΎΠΌ ΡΠ· ΠΊΠΎ-ΡΠ°ΡΠ½Π΄Π΅ΡΠ°ΠΌΠΈ Π·Π±ΠΈΡΠ°ΡΠΌΠΎ ΡΠΈΠ»ΡΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ, ΡΠΎΠ± ΠΏΠ΅ΡΠ΅ΠΌΠ°Π³Π°ΡΠΈ Π½Π° Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΠΈΡ ΡΠΈΠ½ΠΊΠ°Ρ .
Π‘ΡΠΎΠ³ΠΎΠ΄Π½Ρ Π² SKELAR β Π΄Π΅ΡΡΡΠΎΠΊ Π±ΡΠ·Π½Π΅ΡΡΠ² Ρ ΡΡΠ·Π½ΠΈΡ Π½ΡΡΠ°Ρ Π²ΡΠ΄ EdTech Π΄ΠΎ SaaS. Π¦Π΅ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎ ΠΌΠ°ΡΡΡ Π²ΡΠ΄Π·Π½Π°ΠΊΠΈ Π²ΡΠ΄ Product Hunt, ΠΏΠΎΡΡΠ°ΠΏΠ»ΡΡΡΡ Ρ ΡΠ΅ΠΉΡΠΈΠ½Π³ΠΈ Π’ΠΠ-ΡΡΠ°ΡΡΠ°ΠΏΡΠ² ΡΠ° ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²ΠΈΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΠΉ Π£ΠΊΡΠ°ΡΠ½ΠΈ, Π·Π°ΠΉΠΌΠ°ΡΡΡ Π½Π°ΠΉΠ²ΠΈΡΡ ΡΠ°Π±Π»Ρ Π² AppStore ΡΠ° ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ, ΡΠΊΠΈΠΌΠΈ ΠΊΠΎΡΠΈΡΡΡΡΡΡΡΡ ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ Π»ΡΠ΄Π΅ΠΉ. Π ΡΠ΅ ΠΏΡΠΎ Π±ΡΠ·Π½Π΅ΡΠΈ SKELAR ΠΏΠΈΡΡΡΡ TechCrunch, Wired ΡΠ° ΡΠ½ΡΡ ΡΠ²ΡΡΠΎΠ²Ρ ΠΌΠ΅Π΄ΡΠ°.
ΠΠΈΡΠ°ΡΠΌΠΎΡΡ ΡΠΈΠ»ΡΠ½ΠΎΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ ΡΠ· 800+ ΡΠ°Ρ ΡΠ²ΡΡΠ², ΡΠΊΡ ΠΌΠ°ΡΡΡ ΠΊΡΡΡΡ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈΠ·Ρ ΠΉ Π°ΠΌΠ±ΡΡΠ½Ρ ΡΡΠ»Ρ. ΠΠ°ΡΡ Π»ΡΠ΄ΠΈ β Π½Π°ΠΉΡΡΠ½Π½ΡΡΠΈΠΉ Π°ΠΊΡΠΈΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎΠΆ ΠΌΠΈ ΠΎΠ±ΠΈΡΠ°ΡΠΌΠΎ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ Π±ΡΠ·Π½Π΅ΡΠΈ ΡΠ°Π·ΠΎΠΌ Π· Π½Π°ΠΉΠΊΡΠ°ΡΠΈΠΌΠΈ ΡΠ°Π»Π°Π½ΡΠ°ΠΌΠΈ Π½Π° ΡΠΈΠ½ΠΊΡ.
ΠΠ°ΡΠ°Π· ΠΌΠΈ Ρ ΠΏΠΎΡΡΠΊΡ Data Scientist Ρ Π½Π°ΡΡ ΠΏΠΎΡΡΡΠ΅Π»ΡΠ½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ TENTENS Tech.
TENTENS Tech β ΡΠΊΡΠ°ΡΠ½ΡΡΠΊΠ° IT-ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠΎ ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠΈ Ρ ΡΡΠ΅ΡΡ ΡΡΡΡΠΌΡΠ½Π³Ρ ΡΠ° social discovery. ΠΠΎΠΌΠ°Π½Π΄Π° TENTENS Tech ΠΌΠ°Ρ Π΄ΠΎΡΠ²ΡΠ΄ ΡΡΠΏΡΡΠ½ΠΈΡ Π·Π°ΠΏΡΡΠΊΡΠ² Π΄Π΅ΡΡΡΠΊΡΠ² ΠΏΠ»Π°ΡΡΠΎΡΠΌ, ΡΠΊΠΈΠΌΠΈ ΠΊΠΎΡΠΈΡΡΡΡΡΡΡΡ ΠΌΡΠ»ΡΠΉΠΎΠ½ΠΈ Π»ΡΠ΄Π΅ΠΉ Π½Π° Π²ΡΡΡ ΠΊΠΎΠ½ΡΠΈΠ½Π΅Π½ΡΠ°Ρ ΡΠ²ΡΡΡ (ΠΎΠΊΡΡΠΌ ΠΠ½ΡΠ°ΡΠΊΡΠΈΠ΄ΠΈ, ΠΏΠΎΠΊΠΈ ΡΠΎ). Π ΡΠ»ΠΎΠ³Π°Π½ We donβt think limits Π²ΡΠ΄ΠΎΠ±ΡΠ°ΠΆΠ°Ρ ΡΠΊ ΡΡΡΠ°ΡΠ΅Π³ΡΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, ΡΠ°ΠΊ Ρ ΠΌΠΎΡΠΈΠ²Π°ΡΡΡ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Π·Π°Π²ΠΆΠ΄ΠΈ ΠΌΡΠ°ΡΠΈ Π΄Π°Π»Π΅ΠΊΠΎ Π·Π° Π³ΠΎΡΠΈΠ·ΠΎΠ½Ρ. ΠΠ°ΡΠΌΠΎ ΠΏΠΎΡΡΠΆΠ½Ρ ΠΊΠΎΠΌΠ°Π½Π΄ΠΈ Π°Π½Π°Π»ΡΡΠΈΠΊΠΈ Π· 20+ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ².
Π¨ΡΠΊΠ°ΡΠΌΠΎ: ΡΠ°Π»Π°Π½ΠΎΠ²ΠΈΡΠΎΠ³ΠΎ data scientist-Π° Π· Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ ΡΠΎΠ±ΠΎΡΠΈ ΡΡΠΊ+, ΡΠΊΠΈΠΉ full-time Π±ΡΠ΄Π΅ ΡΠ°Π·ΠΎΠΌ Π· ΠΊΠΎΠ»Π΅Π³Π°ΠΌΠΈ ΡΠΎΠ·ΡΠΎΠ±Π»ΡΡΠΈ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ Π΄Π»Ρ ΠΏΠΎΡΡΠ΅Π± Π±ΡΠ·Π½Π΅ΡΡ Ρ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ Π²ΠΆΠ΅ Π½Π°ΡΠ²Π½Ρ. Π£ Π²Π°Ρ Π±ΡΠ΄Π΅ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ ΡΠ· ΡΡΠ·Π½ΠΎΠΌΠ°Π½ΡΡΠ½ΠΈΠΌΠΈ ΡΠΈΠΏΠ°ΠΌΠΈ Π΄Π°Π½ΠΈΡ ΡΠ° ΠΌΠΎΠ΄Π΅Π»ΡΠΌΠΈ, Π·Π°ΡΡΠΎΡΠΎΠ²ΡΠ²Π°ΡΠΈ Π½ΠΎΠ²ΡΡΠ½Ρ ΠΏΡΠ΄Ρ ΠΎΠ΄ΠΈ Π² ΠΎΠ±Π»Π°ΡΡΡ AΠ ΡΠ° ΠΏΡΠΎΠΏΠΎΠ½ΡΠ²Π°ΡΠΈ Π½Π°ΠΉΠΊΡΠ°ΡΡ ΡΡΡΠ΅Π½Π½Ρ Π΄Π»Ρ ΠΏΠΎΡΡΠ΅Π± Π±ΡΠ·Π½Π΅ΡΡ.
Π―ΠΊΡ Π²ΠΈΠΊΠ»ΠΈΠΊΠΈ ΡΠ΅ΠΊΠ°ΡΡΡ Π½Π° ΡΠ΅Π±Π΅ Π² ΡΠΎΠ»Ρ Data Scientist:
β Π ΠΎΠ·Π²ΠΈΡΠΎΠΊ Π²ΠΆΠ΅ Π½Π°ΡΠ²Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Ρ ΡΡ ΡΠ΅ΡΠ²ΡΡΡΠ² (Π°Π½ΡΠΈΡΡΠΎΠ΄, ΠΎΡΡΠ½ΠΊΠ° ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³Ρ, ΠΌΠΎΠ΄Π΅ΡΠ°ΡΡΡ);
β Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Π· Π°Π½Π°Π»ΡΡΠΈΠΊΠ°ΠΌΠΈ, DE-ΡΠΏΠ΅ΡΡΠ°Π»ΡΡΡΠ°ΠΌΠΈ ΡΠ° ΡΠ½ΡΠΈΠΌΠΈ ΡΠ½ΠΆΠ΅Π½Π΅ΡΠ°ΠΌΠΈ Π΄Π»Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ ML-ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ²;
β End-to-end ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ° Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ Π½ΠΎΠ²ΠΈΡ ML-ΡΠ΅ΡΠ²ΡΡΡΠ²;
β ΠΠ°Π±Π΅Π·ΠΏΠ΅ΡΠ΅Π½Π½Ρ ΡΠΊΠΎΡΡΡ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ ΡΠ° ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
β Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° Ρ Π²ΠΏΡΠΎΠ²Π°Π΄ΠΆΠ΅Π½Π½Ρ ΡΠΈΡΡΠ΅ΠΌ ΠΌΠΎΠ½ΡΡΠΎΡΠΈΠ½Π³Ρ ΡΠΎΠ±ΠΎΡΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
Π©ΠΎ Π΄Π»Ρ Π½Π°Ρ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ:
β ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ Π²ΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ SQL ΡΠ° Python;
β ΠΠΏΠ΅Π²Π½Π΅Π½Ρ Π·Π½Π°Π½Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΠ΅ΠΎΡΡΡ ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ ΡΠ° ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ;
β ΠΠ½Π°Π½Π½Ρ ΠΊΠ»Π°ΡΠΈΡΠ½ΠΈΡ ML-Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ²;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· ML-ΡΡΠ΅ΠΉΠΌΠ²ΠΎΡΠΊΠ°ΠΌΠΈ ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ Π΄Π»Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ ;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· GCP/Azure/AWS;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ Ρ ΠΏΡΠ΄ΡΡΠΈΠΌΠΊΠΈ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ/ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
β ΠΠ½Π°Π½Π½Ρ Docker, ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ IaS;
β ΠΠΊΡΡΠ°ΡΠ½ΡΡΡΡ, ΡΠ²Π°Π³Π° Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ, ΠΊΡΠΈΡΠΈΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ;
β ΠΠΌΡΠ½Π½Ρ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π² ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
SKELAR foundation β Π±Π»Π°Π³ΠΎΠ΄ΡΠΉΠ½ΠΈΠΉ ΡΠΎΠ½Π΄, ΡΡΠ²ΠΎΡΠ΅Π½ΠΈΠΉ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. Π ΠΌΠ΅ΠΆΠ°Ρ ΡΠ½ΡΡΡΠ°ΡΠΈΠ²ΠΈ ΡΡΠ²ΠΎΡΡΡΠΌΠΎ ΡΠ° ΡΡΠ½Π°Π½ΡΡΡΠΌΠΎ ΠΏΡΠΎΡΠΊΡΠΈ, ΡΠΎ ΡΠΏΡΠΈΡΡΡΡ ΠΏΠΎΠ΄ΠΎΠ»Π°Π½Π½Ρ Π½Π°ΡΠ»ΡΠ΄ΠΊΡΠ² Π²ΡΠΉΠ½ΠΈ ΡΠ° Π²ΡΠ΄Π½ΠΎΠ²Π»Π΅Π½Π½Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ.
SKELAR β ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ Π΄Π»Ρ ΡΠ°ΠΌΠΎΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ Π»ΡΠ΄Π΅ΠΉ, ΡΠΊΡ Π·Π΄Π°ΡΠ½Ρ ΡΡΠ²ΠΎΡΠΈΡΠΈ ΡΡΠΏΡΡΠ½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ. ΠΠΈ ΡΠ°ΠΊΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π½Π°Π·ΠΈΠ²Π°ΡΠΌΠΎ the next big everything. ΠΡΡΠΈΠΌΠΎ Π² ΡΡ ΠΏΠΎΡΡΠΆΠ½ΡΡΡΡ ΡΠ° ΠΌΠ°ΡΡΡΠ°Π±.ΠΠΈ ΠΏΠ»Π°Π½ΡΡΠΌΠΎ ΠΉ Π½Π°Π΄Π°Π»Ρ ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ tech-Π±ΡΠ·Π½Π΅ΡΠΈ, ΠΏΡΠ΄ΠΊΠΎΡΡΠ²Π°ΡΠΈ Π³Π»ΠΎΠ±Π°Π»ΡΠ½Ρ ΡΠΈΠ½ΠΊΠΈ ΡΠ° ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π·Π°Π΄Π»Ρ ΠΏΠ΅ΡΠ΅ΠΌΠΎΠ³ΠΈ Π£ΠΊΡΠ°ΡΠ½ΠΈ πΊπ¦
ΠΠ»Ρ ΡΡΠΎΠ³ΠΎ ΡΡΠ²ΠΎΡΠΈΠ»ΠΈ Π²ΡΡ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΡ Π²ΡΠ΅ΡΠ΅Π΄ΠΈΠ½Ρ Π½Π°ΡΠΎΠ³ΠΎ Π²Π΅Π½ΡΡΡ Π±ΡΠ»Π΄Π΅ΡΠ°:
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β 8 ΡΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠ½ΠΈΡ ΠΊΠΎΠΌΠ°Π½Π΄, ΡΠΊΡ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°ΡΡΡ Π±ΡΠ·Π½Π΅ΡΠ°ΠΌ Π·Π°ΠΊΡΠΈΠ²Π°ΡΠΈ Π±ΡΠ΄Ρ-ΡΠΊΡ ΠΏΠΈΡΠ°Π½Π½Ρ: Π²ΡΠ΄ ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³Ρ Ρ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΠΉ Π΄ΠΎ ΡΡΠ½Π°Π½ΡΡΠ² ΡΠ° ΡΡΠΈΠ΄ΠΈΡΠ½ΠΈΡ ΠΏΠΈΡΠ°Π½Ρ;
β Π‘ΠΏΡΠ»ΡΠ½ΠΎΡΠ° ΡΠ°ΡΠ½Π΄Π΅ΡΡΠ², ΡΠΊΡ Π²ΠΆΠ΅ Π·Π°ΠΏΡΡΡΠΈΠ»ΠΈ Π½Π΅ ΠΎΠ΄ΠΈΠ½ Π±ΡΠ·Π½Π΅Ρ ΠΉ ΠΌΠΎΠΆΡΡΡ Π΄ΡΠ»ΠΈΡΠΈΡΡ ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΈΠΌ Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ;
β ΠΠ½ΡΡΡΡΡΠ½Ρ ΠΊΠ»ΡΠ±ΠΈ Π·Π° ΠΏΡΠΎΡΠ΅ΡΡΠΉΠ½ΠΈΠΌΠΈ Π½Π°ΠΏΡΡΠΌΠΊΠ°ΠΌΠΈ: ΠΌΠ°ΡΠΊΠ΅ΡΠΈΠ½Π³, ΡΠΎΠ·ΡΠΎΠ±ΠΊΠ°, ΡΡΠ½Π°Π½ΡΠΈ, ΡΠ΅ΠΊΡΡΡΠΈΠ½Π³;
β Π’ΡΠ΅Π½ΡΠ½Π³ΠΈ, ΠΊΡΡΡΠΈ, Π²ΡΠ΄Π²ΡΠ΄ΡΠ²Π°Π½Π½Ρ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΡΠΉ;
β ΠΠ΅Π΄ΠΈΡΠ½Π΅ ΡΡΡΠ°Ρ ΡΠ²Π°Π½Π½Ρ, ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΈΠΉ Π»ΡΠΊΠ°Ρ.
ΠΠ°Π²Π°ΠΉ ΡΠ°Π·ΠΎΠΌ Π±ΡΠ΄ΡΠ²Π°ΡΠΈ the next big everything! -
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Equity Analyst
Ukraine Β· Product Β· 2 years of experience Β· Upper-IntermediateΠΡΠΈΠ²ΡΡ! ΠΠΈ SFORS β ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³ΠΎΠ²Π° ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, Π΄Π΅ ΡΡΠ΅ΠΉΠ΄Π΅ΡΠΈ ΡΡΠ°ΡΡΡ ΡΡΠΏΡΡΠ½ΠΈΠΌΠΈ! Π‘ΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΡΡΠΌΠΎΡΡ Π½Π° ΡΠΎΡΠ³ΡΠ²Π»Ρ Π½Π° ΡΠ²ΡΡΠΎΠ²ΠΈΡ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΈΡ ΡΠΈΠ½ΠΊΠ°Ρ Ρ Ρ ΠΎΠ΄Π½ΠΈΠΌ ΡΠ· ΠΊΠ»ΡΡΠΎΠ²ΠΈΡ Π³ΡΠ°Π²ΡΡΠ² Ρ ΡΡΠ΅ΡΡ ΠΏΡΠΎΠΏ-ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³Ρ ΡΠ° Π»ΡΠ΄Π΅ΡΡΠ² ΠΏΡΠ΅ΠΌΠ°ΡΠΊΠ΅ΡΡ. ΠΠΆΠ΅ 20 ΡΠΎΠΊΡΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ ΡΡΠΏΡΡΠ½Π° Π½Π° ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΌΡ ΡΠΈΠ½ΠΊΡ,...ΠΡΠΈΠ²ΡΡ!
ΠΠΈ SFORS β ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³ΠΎΠ²Π° ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ, Π΄Π΅ ΡΡΠ΅ΠΉΠ΄Π΅ΡΠΈ ΡΡΠ°ΡΡΡ ΡΡΠΏΡΡΠ½ΠΈΠΌΠΈ!
Π‘ΠΏΠ΅ΡΡΠ°Π»ΡΠ·ΡΡΠΌΠΎΡΡ Π½Π° ΡΠΎΡΠ³ΡΠ²Π»Ρ Π½Π° ΡΠ²ΡΡΠΎΠ²ΠΈΡ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΈΡ ΡΠΈΠ½ΠΊΠ°Ρ Ρ Ρ ΠΎΠ΄Π½ΠΈΠΌ ΡΠ· ΠΊΠ»ΡΡΠΎΠ²ΠΈΡ Π³ΡΠ°Π²ΡΡΠ² Ρ ΡΡΠ΅ΡΡ ΠΏΡΠΎΠΏ-ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³Ρ ΡΠ° Π»ΡΠ΄Π΅ΡΡΠ² ΠΏΡΠ΅ΠΌΠ°ΡΠΊΠ΅ΡΡ.
ΠΠΆΠ΅ 20 ΡΠΎΠΊΡΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ ΡΡΠΏΡΡΠ½Π° Π½Π° ΡΠΎΠ½Π΄ΠΎΠ²ΠΎΠΌΡ ΡΠΈΠ½ΠΊΡ, Π·Π°Π²Π΄ΡΠΊΠΈ Π·Π½Π°ΡΠ½ΠΈΠΌ ΡΠ½Π²Π΅ΡΡΠΈΡΡΡΠΌ Ρ ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ ΡΡΠ΅ΠΉΠ΄Π΅ΡΡΠ², ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³ΠΎΠ²Ρ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΡΡ, ΠΏΡΠΎΠ³ΡΠ΅ΡΠΈΠ²Π½Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΡΠΈΠ·ΠΈΠΊ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½ΡΡ ΡΠ° ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΈΡ ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³ΠΎΠ²ΠΈΡ ΡΡΡΠ°ΡΠ΅Π³ΡΠΉ β ΡΡΠ΅ ΡΠ΅, ΡΠΎ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°Ρ ΡΡΠ΅ΠΉΠ΄Π΅ΡΠ°ΠΌ ΡΡΠ°ΡΠΈ ΡΡΠΏΡΡΠ½ΠΈΠΌΠΈ.
ΠΡΠΈ ΡΡΠΎΠΌΡ SFORS ΠΎΠΏΠ΅ΡΡΡ Π²ΠΈΠΊΠ»ΡΡΠ½ΠΎ Π²Π»Π°ΡΠ½ΠΈΠΌΠΈ ΠΊΠΎΡΡΠ°ΠΌΠΈ ΡΠ° Π½Π΅ Π·Π°Π»ΡΡΠ°Ρ ΡΡΠΎΡΠΎΠ½Π½ΡΡ ΡΠ½Π²Π΅ΡΡΠΈΡΡΠΉ.
Π£ ΠΊΠΎΠΌΠ°Π½Π΄Ρ Trading Research & Development ΡΡΠΊΠ°ΡΠΌΠΎ Research Analyst Π΄Π»Ρ ΠΏΠΎΠΊΡΠΈΡΡΡ research- ΡΠ° quant-ΠΏΡΠΎΡΠ΅ΡΡΠ² Ρ Π²ΡΠ΄Π΄ΡΠ»Ρ.
Π―ΠΊΡΠΎ ΡΠΈ ΠΎΠ±Π΅ΡΠ΅Ρ ΡΡ Π²Π°ΠΊΠ°Π½ΡΡΡ, ΠΎΡΡ ΡΠΎ ΡΠΈ ΡΠΎΠ±ΠΈΡΠΈΠΌΠ΅Ρ:
- ΠΠΎΡΠ»ΡΠ΄ΠΆΡΠΉ ΡΠ²ΡΡΠΎΠ²Ρ ΡΡΠ½Π°Π½ΡΠΎΠ²Ρ ΡΠΈΠ½ΠΊΠΈ.
Π©ΠΎΠ΄Π½Ρ Π²ΡΠ΄ΡΡΠ΅ΠΆΡΠΉ Π°ΠΊΡΡΡ ΡΡΠ·Π½ΠΈΡ ΠΊΡΠ°ΡΠ½ ΡΠ²ΡΡΡ, Π·ΠΎΠΊΡΠ΅ΠΌΠ° ΠΏΡΠΎΠ²ΡΠ΄Π½ΠΈΡ ΡΠ΅ΠΊΡΠΎΡΡΠ² Π‘Π¨Π. ΠΠΎΠΌΠ±ΡΠ½ΡΠΉ Π³Π΅ΠΎΠΏΠΎΠ»ΡΡΠΈΡΠ½Ρ ΡΠ° ΠΌΠ°ΠΊΡΠΎ ΠΏΠΎΠ΄ΡΡ, ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½Ρ ΡΠ΅Π»ΡΠ·ΠΈ ΡΠ° ΠΏΠΎΡΠΎΠΊΠΎΠ²Ρ Π΄Π°Π½Ρ, Π°Π±ΠΈ Π²Π»ΠΎΠ²ΠΈΡΠΈ ΡΠΈΠ½ΠΊΠΎΠ²ΠΈΠΉ ΡΠΌΠΏΡΠ»ΡΡ ΡΠ°Π½ΡΡΠ΅ Π·Π° ΡΠ½ΡΠΈΡ .
- ΠΠΈΡΠ²Π»ΡΠΉ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎΡΡΡ ΡΠ° ΡΠΈΡΡΠ΅ΠΌΠ½Ρ Π²Π·Π°ΡΠΌΠΎΠ·Π²βΡΠ·ΠΊΠΈ.
Π€ΠΎΡΠΌΡΠ»ΡΠΉ Π²Π»Π°ΡΠ½Ρ Π³ΡΠΏΠΎΡΠ΅Π·ΠΈ ΡΠΎΠ΄ΠΎ ΠΏΠΎΠ²Π΅Π΄ΡΠ½ΠΊΠΈ ΡΠ΅ΠΊΡΠΎΡΡΠ² ΡΠ° ΠΎΠΊΡΠ΅ΠΌΠΈΡ ΠΊΠΎΠΌΠΏΠ°Π½ΡΠΉ, Π±ΡΠ΄ΡΠΉ ΡΡΠ΅Π½Π°ΡΡΡ ΠΉ ΠΏΠ΅ΡΠ΅Π²ΡΡΡΠΉ ΡΡ Π½Π° ΡΠ΅Π°Π»ΡΠ½ΠΈΡ ΡΠΈΠ½ΠΊΠΎΠ²ΠΈΡ ΡΡΡ Π°Ρ .
- ΠΠ±ΠΈΡΠ°ΠΉ ΡΠ° Π°Π½Π°Π»ΡΠ·ΡΠΉ Π΄Π°Π½Ρ.
ΠΡΠ°ΡΡΠΉ ΡΠ· Π·Π²ΡΡΠ°ΠΌΠΈ ΠΊΠΎΠΌΠΏΠ°Π½ΡΠΉ, Π³Π°Π»ΡΠ·Π΅Π²ΠΈΠΌΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½ΡΠΌΠΈ ΡΠ° Π°Π»ΡΡΠ΅ΡΠ½Π°ΡΠΈΠ²Π½ΠΎΡ data. ΠΠΈΠΊΠΎΡΠΈΡΡΠΎΠ²ΡΠΉ ΡΡΠ΅, ΡΠΎ Π΄Π°Ρ edge.
- Π€ΡΠ»ΡΡΡΡΠΉ, ΠΌΠΎΠ΄Π΅Π»ΡΠΉ, ΠΏΠ΅ΡΠ΅Π²ΡΡΡΠΉ.
ΠΡΠ΄ΡΠΉ Π΅ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΡΠΈΡΠ½Ρ ΡΠ° ML-ΠΌΠΎΠ΄Π΅Π»Ρ, ΠΏΠ΅ΡΠ΅ΡΠ²ΠΎΡΡΡΡΠΈ ΡΠ΄Π΅Ρ Π½Π° ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ²Π°Π½Ρ ΡΠΈΠ³Π½Π°Π»ΠΈ. ΠΡΠ΄Π΄Π°Π²Π°ΠΉ ΡΡ stress-ΡΠ΅ΡΡΠ°ΠΌ ΡΠ΅Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠΈΠ½ΠΊΡ ΠΉ Π·Π°Π»ΠΈΡΠ°ΠΉ Π»ΠΈΡΠ΅ ΡΠ΅, ΡΠΎ ΡΠ΅Π°Π»ΡΠ½ΠΎ ΠΏΡΠ°ΡΡΡ.
- ΠΡΡΡΠ°Π²Π°ΠΉΡΡ ΡΡΡΡ.
ΠΡΠ΄Π΄ΡΠ»ΡΠΉ ΠΊΠ»ΡΡΠΎΠ²Ρ Π΄ΡΠ°ΠΉΠ²Π΅ΡΠΈ Π²ΡΠ΄ ΡΠ½ΡΠΎΡΠΌΠ°ΡΡΠΉΠ½ΠΎΠ³ΠΎ ΡΡΠΌΡ.
- ΠΠΎΡΡΠ½ΡΠΉ Π·ΡΠΎΠ·ΡΠΌΡΠ»ΠΎ ΡΠ° Π»Π°ΠΊΠΎΠ½ΡΡΠ½ΠΎ.
ΠΠ΅ΡΠ΅ΡΠ²ΠΎΡΡΠΉ ΠΌΠ°ΡΠΈΠ²ΠΈ ΡΠΈΡΡ Π½Π° Π΄Π°ΡΠ±ΠΎΡΠ΄ΠΈ, Π³ΡΠ°ΡΡΠΊΠΈ ΡΠ° Π»Π°ΠΊΠΎΠ½ΡΡΠ½Ρ ΡΡΠ°ΡΡΡ, ΡΠΊΠΈΠΌΠΈ ΡΡΠ΅ΠΉΠ΄Π΅Ρ ΠΌΠΎΠΆΠ΅ ΡΠΊΠΎΡΠΈΡΡΠ°ΡΠΈΡΡ Π½Π΅Π³Π°ΠΉΠ½ΠΎ. ΠΡΠΎΠ²ΠΎΠ΄Ρ Π²Π½ΡΡΡΡΡΠ½Ρ Π»Π΅ΠΊΡΡΡ ΠΉ ΠΏΠΎΡΠΈΡΡΠΉ Π·Π½Π°Π½Π½Ρ Π² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ.
- ΠΠ΄ΠΎΡΠΊΠΎΠ½Π°Π»ΡΠΉΡΡ ΡΠΎΠ΄Π½Ρ.
Π ΠΈΠ½ΠΎΠΊ Π·ΠΌΡΠ½ΡΡΡΡΡΡ ΡΠΎΡ Π²ΠΈΠ»ΠΈΠ½ΠΈ β ΠΎΠΏΠ°Π½ΠΎΠ²ΡΠΉ Π½ΠΎΠ²Ρ ΡΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΈ ΠΉ Π·Π²ΡΡΡΠΉ ΡΠ²ΠΎΡ Π±Π°ΡΠ΅Π½Π½Ρ Π· ΠΏΠΎΡΠΎΡΠ½ΠΈΠΌΠΈ ΠΏΠΎΠ΄ΡΡΠΌΠΈ, ΡΠΎΠ± ΡΠ²ΡΠΉ edge Π·Π°Π»ΠΈΡΠ°Π²ΡΡ ΡΠ²ΡΠΆΠΈΠΌ Ρ Π³ΠΎΡΡΡΠΈΠΌ.
- ΠΠΏΡΠΈΠΌΡΠ·ΡΠΉ ΠΏΡΠΎΡΠ΅ΡΠΈ ΡΠ° ΡΠ½ΡΡΡΡΠΉ Π½ΠΎΠ²Ρ ΠΏΡΠΎΡΠΊΡΠΈ.
ΠΠΎΠ±Π°ΡΠΈΠ², Π΄Π΅ ΠΌΠΎΠΆΠ½Π° ΠΏΠΎΠΊΡΠ°ΡΠΈΡΠΈ ΠΏΠΎΡΠΎΡΠ½ΠΈΠΉ ΠΏΡΠΎΡΠ΅Ρ ΡΠΈ ΡΡΠ²ΠΎΡΠΈΡΠΈ Π½ΠΎΠ²ΠΈΠΉ? ΠΡΠ΄Π³ΠΎΡΡΠΉ Π°ΡΠ³ΡΠΌΠ΅Π½ΡΠΎΠ²Π°Π½ΠΈΠΉ ΠΊΠ΅ΠΉΡβ Ρ ΠΏΠ΅ΡΠ΅Π²ΡΡΠΈΠΌΠΎ ΠΉΠΎΠ³ΠΎ Π² Π΄ΡΡ ΡΠ°Π·ΠΎΠΌ.
ΠΡΡΠΊΡΠ²Π°Π½Ρ Π·Π½Π°Π½Π½Ρ ΡΠ° Π½Π°Π²ΠΈΡΠΊΠΈ:
Hard Skills
- ΠΠΊΡΠΏΠ΅ΡΡΠΈΠ·Π° (2Ρ.+) Π² ΠΎΠ΄Π½ΠΎΠΌΡ Π°Π±ΠΎ Π΄Π΅ΠΊΡΠ»ΡΠΊΠΎΡ
Π½Π°ΠΏΡΡΠΌΠ°Ρ
:
- Financial Analysis
- Macroeconomic Analysis
- Quantitative Analysis
- Investment & Asset Management
- ΠΠ»ΠΈΠ±ΠΎΠΊΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΡΡΠ½Π°Π½ΡΠΎΠ²ΠΈΡ ΡΠΈΠ½ΠΊΡΠ², ΠΎΡΠΎΠ±Π»ΠΈΠ²ΠΎ Π² US Equity.
- ΠΠΎΠ»ΠΎΠ΄ΡΠ½Π½Ρ Python ΡΠ° SQL Π΄Π»Ρ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π΄Π°Π½ΠΈΡ .
- Π ΠΎΠ±ΠΎΡΠ° Π· Bloomberg Terminal β Π±ΡΠ΄Π΅ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ.
- ΠΠ½Π³Π»ΡΠΉΡΡΠΊΠ°: B2 Ρ Π²ΠΈΡΠ΅.
Soft Skills
- ΠΠ½Π°Π»ΡΡΠΈΡΠ½Π΅ ΠΌΠΈΡΠ»Π΅Π½Π½Ρ ΡΠ° Π΄ΠΎΠΏΠΈΡΠ»ΠΈΠ²ΡΡΡΡ.
- ΠΠΎΠΌΠ°Π½Π΄Π½Π° ΡΠΎΠ±ΠΎΡΠ°, ΡΠ°ΠΌΠΎΡΡΡΠΉΠ½ΡΡΡΡ, ΡΠ½ΡΡΡΠ°ΡΠΈΠ²Π½ΡΡΡΡ.
- ΠΠΈΡΠΎΠΊΠΈΠΉ ΡΡΠ²Π΅Π½Ρ ΡΠ°ΠΌΠΎΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΡΡ ΡΠ° ΡΠΈΡΡΠ΅ΠΌΠ½ΠΎΡΡΡ.
ΠΠΈΠΌΠΎΠ³ΠΈ Π΄ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ
- Middle: 2+ ΡΠΎΠΊΠΈ Π΄ΠΎΡΠ²ΡΠ΄Ρ Π² Π°Π½Π°Π»ΡΡΠΈΡΡ Π°Π±ΠΎ Π°ΠΊΡΠΈΠ²Π½ΠΎΠΌΡ ΡΡΠ΅ΠΉΠ΄ΠΈΠ½Π³Ρ.
- Senior: 4+ ΡΠΎΠΊΠΈ Π°Π½Π°Π»ΠΎΠ³ΡΡΠ½ΠΎΠ³ΠΎ Π΄ΠΎΡΠ²ΡΠ΄Ρ.
ΠΡΠΆΠ½Π°ΡΠΎΠ΄Π½Π° ΡΠ΅ΡΡΠΈΡΡΠΊΠ°ΡΡΡ (CFA, FRM, CAIA, ACCA ΡΠΎΡΠΎ) β ΠΏΠ΅ΡΠ΅Π²Π°Π³Π° Π°Π±ΠΎ ΠΏΡΠΎΡΠ΅Ρ ΡΡ Π·Π΄ΠΎΠ±ΡΡΡΡ.
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Β· 48 views Β· 3 applications Β· 26d
Data Scientist (NLP + Recommender Systems)
Ukraine Β· Product Β· 3 years of experience Ukrainian Product πΊπ¦ΠΠΎΠΌΠ°Π½Π΄Π° MEGOGO ΡΡΠΊΠ°Ρ Data Scientist, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΠΈΡΠΈ Π½Π°ΡΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ, ΠΏΠΎΡΡΠΊΡ ΡΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ. Π―ΠΊΡΠΎ ΡΠΎΠ±Ρ ΡΡΠΊΠ°Π²ΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ², Π·Π°ΡΡΠΎΡΠΎΠ²ΡΠ²Π°ΡΠΈ NLP-ΠΌΠΎΠ΄Π΅Π»Ρ Π² ΠΏΡΠΎΠ΄Π°ΠΊΡΠ΅Π½Ρ ΡΠ° ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΡΡΡΠ°ΡΠ½Ρ...ΠΠΎΠΌΠ°Π½Π΄Π° MEGOGO ΡΡΠΊΠ°Ρ Data Scientist, ΡΠΊΠΈΠΉ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»ΠΈΡΠΈ Π½Π°ΡΡ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ, ΠΏΠΎΡΡΠΊΡ ΡΠ° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ. Π―ΠΊΡΠΎ ΡΠΎΠ±Ρ ΡΡΠΊΠ°Π²ΠΎ ΠΏΡΠ°ΡΡΠ²Π°ΡΠΈ Π· ΡΠ΅Π°Π»ΡΠ½ΠΈΠΌΠΈ Π΄Π°Π½ΠΈΠΌΠΈ ΠΌΡΠ»ΡΠΉΠΎΠ½ΡΠ² ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠ², Π·Π°ΡΡΠΎΡΠΎΠ²ΡΠ²Π°ΡΠΈ NLP-ΠΌΠΎΠ΄Π΅Π»Ρ Π² ΠΏΡΠΎΠ΄Π°ΠΊΡΠ΅Π½Ρ ΡΠ° ΡΠΎΠ·Π²ΠΈΠ²Π°ΡΠΈ ΡΡΡΠ°ΡΠ½Ρ Recommender Systems β ΠΏΡΠΈΡΠ΄Π½ΡΠΉΡΡ.
Π©ΠΎ Π½Π° ΡΠ΅Π±Π΅ ΠΎΡΡΠΊΡΡ:
- Π ΠΎΠ·ΡΠΎΠ±ΠΊΠ° ΡΠ° ΡΠΎΠ·Π²ΠΈΡΠΎΠΊ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉ Π΄Π»Ρ ΡΡΠ·Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ;
- ΠΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½Ρ NLP-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π΄Π»Ρ ΠΏΠΎΠΊΡΠ°ΡΠ΅Π½Π½Ρ ΠΏΠΎΡΡΠΊΡ ΡΠ° ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ;
- Π£ΡΠ°ΡΡΡ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ², ΠΏΠΎΠ²'ΡΠ·Π°Π½ΠΈΡ Π· Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Π½ΡΠΌ NLP Π½Π°ΠΏΡΡΠΌΠΊΡ(Π°Π½Π°Π»ΡΠ·, ΠΏΠΎΡΡΠΊ, ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ Π²Π»Π°ΡΠ½ΠΈΡ ΡΡΡ Π· ΡΠ΅ΠΊΡΡΠΎΠ²ΠΎΡ ΡΠΊΠ»Π°Π΄ΠΎΠ²ΠΎΡ, ΡΠΎΡΠΎ);
- Π ΠΎΠ±ΠΎΡΠ° Π· Π²Π΅Π»ΠΈΠΊΠΈΠΌΠΈ ΠΌΠ°ΡΠΈΠ²Π°ΠΌΠΈ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠΊΠΈΡ Π΄Π°Π½ΠΈΡ Π΄Π»Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²ΠΈ ΡΠ½ΡΠΊΠ°Π»ΡΠ½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ·Π°ΡΡΡ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡ;
- Π£ΡΠ°ΡΡΡ Ρ ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Ρ ΡΠ° Π²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ ΠΏΠ°ΠΉΠΏΠ»Π°ΠΉΠ½ΡΠ² Π΄Π»Ρ ΡΡΠ΅Π½ΡΠ²Π°Π½Π½Ρ ΡΠ° Π΄Π΅ΠΏΠ»ΠΎΡ ML-ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ;
- Π’Π΅ΡΡΡΠ²Π°Π½Π½Ρ Π³ΡΠΏΠΎΡΠ΅Π·, Π·Π°ΠΏΡΡΠΊ A/B Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ² ΡΠ° ΡΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°ΡΡΡ ΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΠ²;
- Π‘ΠΏΡΠ²ΠΏΡΠ°ΡΡ Ρ ΠΎΠ±ΠΌΡΠ½ Π΄ΠΎΡΠ²ΡΠ΄ΠΎΠΌ Π· ΡΠ½ΡΠΈΠΌΠΈ DS-ΡΠ°Ρ ΡΠ²ΡΡΠΌΠΈ Π΄Π»Ρ Π²ΠΈΡΡΡΠ΅Π½Π½Ρ Π·Π°Π΄Π°Ρ Π² ΡΡΠΌΡΠΆΠ½ΠΈΡ Π½Π°ΠΏΡΡΠΌΠΊΠ°Ρ .
ΠΠ΅ΠΎΠ±Ρ ΡΠ΄Π½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄:
- 3+ ΡΠΎΠΊΠΈ Π΄ΠΎΡΠ²ΡΠ΄Ρ ΡΠΎΠ±ΠΎΡΠΈ Π² ΡΠΎΠ»Ρ Data Scientist Π°Π±ΠΎ ML Engineer;
- ΠΠΏΠ΅Π²Π½Π΅Π½Π΅ Π·Π½Π°Π½Π½Ρ Python ΡΠ° Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊ Π΄Π»Ρ Π°Π½Π°Π»ΡΠ·Ρ Π΄Π°Π½ΠΈΡ ΡΠ° ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ (Pandas, Scikit-learn, PyTorch Π°Π±ΠΎ TensorFlow);
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ² ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΠΉΠ½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ: collaborative filtering, matrix factorization, content-based methods, hybrid models, RL;
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· NLP-ΠΏΡΠ΄Ρ ΠΎΠ΄Π°ΠΌΠΈ: embedding models, text classification, entity recognition, transformers;
- ΠΠΎΡΠ²ΡΠ΄ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ ΡΠΊΠ»Π°Π΄Π½ΠΈΡ EDA Π½Π° ΡΠ΅Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ ;
- ΠΠ°Π·ΠΎΠ²ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Π· MLOps-ΠΏΡΠ΄Ρ ΠΎΠ΄Π°ΠΌΠΈ;
- Π ΠΎΠ·ΡΠΌΡΠ½Π½Ρ A/B ΡΠ΅ΡΡΡΠ²Π°Π½Π½Ρ, ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ½ΠΈΡ Π³ΡΠΏΠΎΡΠ΅Π·;
- ΠΠΎΠ±ΡΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ SQL ΡΠ° Π°Π½Π°Π»ΡΠ·Π° Π΄Π°Π½ΠΈΡ : ΡΠΎΠ±ΠΎΡΠ° Π· Π±ΡΠ΄Ρ-ΡΠΊΠΈΠΌΠΈ Π΄ΠΆΠ΅ΡΠ΅Π»Π°ΠΌΠΈ Π΄Π°Π½ΠΈΡ (SQL, noSQL, Π²Π΅ΠΊΡΠΎΡΠ½Ρ Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , column-oriented Π±Π°Π·ΠΈ Π΄Π°Π½ΠΈΡ , ΡΠΎΡΠΎ).
Π¨ΡΠΊΠ°ΡΠΌΠΎ Π»ΡΠ΄ΠΈΠ½Ρ, ΡΠΊΠ°:
- ΠΠ°Ρ Π±Π°ΠΆΠ°Π½Π½Ρ ΡΡΠ°Π²Π°ΡΠΈ ΡΠΈΠ»ΡΠ½ΡΡΠ΅ ΡΠ°Π·ΠΎΠΌ Π· ΠΊΠΎΠΌΠ°Π½Π΄ΠΎΡ;
- ΠΠ°Ρ RnD mindset: ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΊΠΎΠ»ΠΈ ΡΡΠ΅Π±Π° Π·ΡΠΎΠ±ΠΈΡΠΈ ΡΠ²ΠΈΠ΄ΠΊΠΎ Π΄Π»Ρ ΠΏΠ΅ΡΠ΅Π²ΡΡΠΊΠΈ Π³ΡΠΏΠΎΡΠ΅Π·ΠΈ Ρ ΠΊΠΎΠ»ΠΈ Π΄ΡΠΆΠ΅ ΡΠΊΡΡΠ½ΠΎ, Π±ΠΎ Π²ΡΠ΄ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π·Π°Π»Π΅ΠΆΠΈΡΡ ΠΊΠΎΡΠΈΡΡΡΠ²Π°ΡΡΠΊΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ ΡΠΈΡΡΡ Π»ΡΠ΄Π΅ΠΉ;
- ΠΠΎΡΠΎΠ²Π° Π²ΡΠ΄ΠΊΡΠΈΡΠΎ Π²ΠΈΡΠ»ΠΎΠ²Π»ΡΠ²Π°ΡΠΈ Π±ΡΠ΄Ρ-ΡΠΊΡ ΡΠ²ΠΎΡ Π΄ΡΠΌΠΊΠΈ.
ΠΡΠ΄Π΅ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ:
- ΠΠΎΡΠ²ΡΠ΄ Π· Elasticsearch Π°Π±ΠΎ ΡΠ½ΡΠΈΠΌΠΈ ΠΏΠΎΡΡΠΊΠΎΠ²ΠΈΠΌΠΈ ΡΠΈΡΡΠ΅ΠΌΠ°ΠΌΠΈ;
- ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π°Π»Π³ΠΎΡΠΈΡΠΌΠ°ΠΌΠΈ Π³Π»ΠΈΠ±ΠΎΠΊΠΎΠ³ΠΎ Π½Π°Π²ΡΠ°Π½Π½Ρ: transformers, reinforcement learning, autoencoders, ΡΠΎΡΠΎ.
Π©ΠΎ ΠΌΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
- Π ΠΎΠ±ΠΎΡΡ Π² ΡΡΠ°Π±ΡΠ»ΡΠ½ΡΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ β Π°Π΄ΠΆΠ΅ ΠΌΠΈ ΠΏΠΎΠ½Π°Π΄ 10 ΡΠΎΠΊΡΠ² Π½Π° ΡΠΈΠ½ΠΊΡ;
- ΠΡΠΉΡΠ½ΠΎ ΡΡΠΊΠ°Π²Ρ Π·Π°Π²Π΄Π°Π½Π½Ρ: Π±Π΅ΡΠΈ ΡΡΠ°ΡΡΡ Ρ ΡΡΠ²ΠΎΡΠ΅Π½Π½Ρ ΠΌΠ΅Π΄ΡΠ°ΡΠ΅ΡΠ²ΡΡΡ ΠΌΠ°ΠΉΠ±ΡΡΠ½ΡΠΎΠ³ΠΎ;
- ΠΡΠ΄Π½ΠΎΡΠΈΠ½ΠΈ, ΠΏΠΎΠ±ΡΠ΄ΠΎΠ²Π°Π½Ρ Π½Π° Π΄ΠΎΠ²ΡΡΡ;
- ΠΠ°Π³Π°ΡΠΎ ΠΌΠΎΠΆΠ»ΠΈΠ²ΠΎΡΡΠ΅ΠΉ Π΄Π»Ρ ΡΠΎΠ·Π²ΠΈΡΠΊΡ;
- ΠΠ΅ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎ ΠΊΡΡΡΡ ΠΊΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²ΠΈ;
- ΠΠ΅Π·ΠΊΠΎΡΡΠΎΠ²Π½Ρ ΡΡΠΎΠΊΠΈ Π°Π½Π³Π»ΡΠΉΡΡΠΊΠΎΡ ΠΌΠΎΠ²ΠΈ;
- ΠΠ°Π½ΡΡΡΡ Π· ΠΏΠ»Π°Π²Π°Π½Π½Ρ, Π° ΡΠ°ΠΊΠΎΠΆ ΡΡΠΎΠΊΠΈ Π½Π°ΡΡΠΎΠ»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅Π½ΡΡΡ;
- ΠΠΎΡΠΏΠΎΡΠ°ΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³Π°;
ΠΠ»Ρ ΡΠΏΡΠ²ΡΠΎΠ±ΡΡΠ½ΠΈΠΊΡΠ² ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π·Π½ΠΈΠΆΠΊΠΈ Π²ΡΠ΄ Π±ΡΠ΅Π½Π΄ΡΠ² ΠΏΠ°ΡΡΠ½Π΅ΡΡΠ².
ΠΠΈ ΠΏΡΠ°Π³Π½Π΅ΠΌΠΎ Π±ΡΡΠΈ ΡΠΎΠ±ΠΎΡΠΎΠ΄Π°Π²ΡΠ΅ΠΌ, ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΠΈΡΠ°ΡΡΡ.
ΠΡΠ΄Π΅ΠΌΠΎ Π²Π΄ΡΡΠ½Ρ, ΡΠΊΡΠΎ Π·Π°ΠΏΠΎΠ²Π½ΠΈΡ ΠΊΠΎΡΠΎΡΠΊΠ΅ ΠΎΠΏΠΈΡΡΠ²Π°Π½Π½Ρ ΠΏΡΠΎ ΡΠ΅, ΡΠΎ Π΄Π»Ρ ΡΠ΅Π±Π΅ Π²Π°ΠΆΠ»ΠΈΠ²ΠΎ. Π¦Π΅ Π΄ΠΎΠΏΠΎΠΌΠΎΠΆΠ΅ Π½Π°ΠΌ ΠΊΡΠ°ΡΠ΅ ΡΠΎΠ·ΡΠΌΡΡΠΈ ΠΎΡΡΠΊΡΠ²Π°Π½Π½Ρ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΡΠ² Ρ ΡΡΠ²ΠΎΡΡΠ²Π°ΡΠΈ ΡΠ΅ Π±ΡΠ»ΡΡ ΠΊΠΎΠΌΡΠΎΡΡΠ½Π΅ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ Π² MEGOGO.
ΠΠΎΡΠΈΠ»Π°Π½Π½Ρ ΡΡΡ - https://bit.ly/43YaxBH
ΠΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π°ΡΡΠΈ Π½Π° Π²Π°ΠΊΠ°Π½ΡΡΡ Ρ Π½Π°Π΄ΡΡΠ»Π°Π²ΡΠΈ ΡΠ²ΠΎΡ ΡΠ΅Π·ΡΠΌΠ΅ Π² ΠΠΎΠΌΠΏΠ°Π½ΡΡ (Π’ΠΠ Β«ΠΠΠΠΠΠΒ»), Π·Π°ΡΠ΅ΡΡΡΡΠΎΠ²Π°Π½Ρ ΠΉ Π΄ΡΡΡΡ Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ Π·Π°ΠΊΠΎΠ½ΠΎΠ΄Π°Π²ΡΡΠ²Π° Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΡΠ΅ΡΡΡΡΠ°ΡΡΠΉΠ½ΠΈΠΉ Π½ΠΎΠΌΠ΅Ρ 38347009, Π°Π΄ΡΠ΅ΡΠ°: Π£ΠΊΡΠ°ΡΠ½Π°, 01011, ΠΌΡΡΡΠΎ ΠΠΈΡΠ², Π²ΡΠ».Π ΠΈΠ±Π°Π»ΡΡΡΠΊΠ°, Π±ΡΠ΄ΠΈΠ½ΠΎΠΊ 22 (Π΄Π°Π»Ρ Β«ΠΠΎΠΌΠΏΠ°Π½ΡΡΒ»), Π²ΠΈ ΠΏΡΠ΄ΡΠ²Π΅ΡΠ΄ΠΆΡΡΡΠ΅ ΡΠ° ΠΏΠΎΠ³ΠΎΠ΄ΠΆΡΡΡΠ΅ΡΡ Π· ΡΠΈΠΌ, ΡΠΎ ΠΠΎΠΌΠΏΠ°Π½ΡΡ ΠΎΠ±ΡΠΎΠ±Π»ΡΡ Π²Π°ΡΡ ΠΎΡΠΎΠ±ΠΈΡΡΡ Π΄Π°Π½Ρ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ Ρ Π²Π°ΡΠΎΠΌΡ ΡΠ΅Π·ΡΠΌΠ΅, Π²ΡΠ΄ΠΏΠΎΠ²ΡΠ΄Π½ΠΎ Π΄ΠΎ ΠΠ°ΠΊΠΎΠ½Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ Β«ΠΡΠΎ Π·Π°Ρ ΠΈΡΡ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΠΈΡ Π΄Π°Π½ΠΈΡ Β» ΡΠ° ΠΏΡΠ°Π²ΠΈΠ» GDPR.
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Data Scientist
Office Work Β· Ukraine (Kyiv) Β· Product Β· 3 years of experienceΠΡΡΠ°ΡΠΌΠΎ Π² King Group ΠΌΡΡΡΡ, Π΄Π΅ Π·ΡΡΡΡΡΡΠ°ΡΡΡΡΡ Π½Π°ΠΉΠΊΡΠ°ΡΡ Π»ΡΠ΄ΠΈ Π· IT- ΡΠ° Π³Π΅ΠΌΠ±Π»ΡΠ½Π³-ΡΠ½Π΄ΡΡΡΡΡΡ, ΡΠΎΠ± ΡΠ°Π·ΠΎΠΌ ΡΠΎΠ±ΠΈΡΠΈ Π΄ΠΈΠ²ΠΎΠ²ΠΈΠΆΠ½Ρ ΡΠ΅ΡΡ. ΠΠΈ ΠΎΠΏΠ΅ΡΡΡΠΌΠΎ ΡΠΈΡΠ»Π΅Π½Π½ΠΈΠΌΠΈ ΠΏΡΠΎΡΠΊΡΠ°ΠΌΠΈ Ρ ΡΡΠ΅ΡΡ iGaming Π½Π° ΡΠΈΠ½ΠΊΠ°Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΠΠ²ΡΠΎΠΏΠΈ ΡΠ° Π‘Π¨Π, ΡΠ½Π²Π΅ΡΡΡΡΠΌΠΎ Ρ Π²Π΅Π½ΡΡΡΠ½Ρ ΡΡΠ°ΡΡΠ°ΠΏΠΈ, ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½Ρ ΡΠ΄Π΅Ρ...ΠΡΡΠ°ΡΠΌΠΎ Π² King Group γΌ ΠΌΡΡΡΡ, Π΄Π΅ Π·ΡΡΡΡΡΡΠ°ΡΡΡΡΡ Π½Π°ΠΉΠΊΡΠ°ΡΡ Π»ΡΠ΄ΠΈ Π· IT- ΡΠ° Π³Π΅ΠΌΠ±Π»ΡΠ½Π³-ΡΠ½Π΄ΡΡΡΡΡΡ, ΡΠΎΠ± ΡΠ°Π·ΠΎΠΌ ΡΠΎΠ±ΠΈΡΠΈ Π΄ΠΈΠ²ΠΎΠ²ΠΈΠΆΠ½Ρ ΡΠ΅ΡΡ. ΠΠΈ ΠΎΠΏΠ΅ΡΡΡΠΌΠΎ ΡΠΈΡΠ»Π΅Π½Π½ΠΈΠΌΠΈ ΠΏΡΠΎΡΠΊΡΠ°ΠΌΠΈ Ρ ΡΡΠ΅ΡΡ iGaming Π½Π° ΡΠΈΠ½ΠΊΠ°Ρ Π£ΠΊΡΠ°ΡΠ½ΠΈ, ΠΠ²ΡΠΎΠΏΠΈ ΡΠ° Π‘Π¨Π, ΡΠ½Π²Π΅ΡΡΡΡΠΌΠΎ Ρ Π²Π΅Π½ΡΡΡΠ½Ρ ΡΡΠ°ΡΡΠ°ΠΏΠΈ, ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½Ρ ΡΠ΄Π΅Ρ ΡΠ° Π»ΡΠ΄Π΅ΠΉ.
ΠΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎ Π·ΡΠΎΡΡΠ°ΡΠΌΠΎ ΡΠ° ΡΠΎΠ·ΡΠΈΡΡΡΠΌΠΎΡΡ, ΡΡΠΏΡΡΠ½ΠΎ Π·Π°ΠΏΡΡΡΠΈΠ²ΡΠΈ ΡΠ° ΡΠΎΠ·ΡΠΈΡΠΈΠ²ΡΠΈ Π½ΠΈΠ·ΠΊΡ Π½ΠΎΠ²ΠΈΡ ΠΏΡΠΎΠ΄ΡΠΊΡΡΠ² ΠΏΡΠΎΡΡΠ³ΠΎΠΌ ΠΎΡΡΠ°Π½Π½ΡΠΎΠ³ΠΎ ΡΠΎΠΊΡ.
ΠΠ°ΡΠ°Π·Ρ ΠΌΠΈ Ρ ΠΏΠΎΡΡΠΊΠ°Ρ Data Scientist, ΡΠΎ Π΄ΠΎΡΠ΄Π½Π°ΡΡΡΡΡ ΡΠ° ΠΏΡΠ΄ΡΠΈΠ»ΠΈΡΡ Π½Π°ΡΡ Analytics & Insights ΠΊΠΎΠΌΠ°Π½Π΄Ρ.
ΠΡΠ½ΠΎΠ²Π½Ρ Π²ΠΈΠΌΠΎΠ³ΠΈ:
β Π‘ΡΡΠΏΡΠ½Ρ Π±Π°ΠΊΠ°Π»Π°Π²ΡΠ°/ΠΌΠ°Π³ΡΡΡΡΠ° Π°Π±ΠΎ Π΅ΠΊΠ²ΡΠ²Π°Π»Π΅Π½ΡΠ½ΠΈΠΉ Π΄ΠΎΡΠ²ΡΠ΄ Ρ Π³Π°Π»ΡΠ·Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ, ΡΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΊΠΈ ΡΠΈ ΡΡΠΌΡΠΆΠ½ΠΈΡ Π³Π°Π»ΡΠ·Π΅ΠΉ;
β ΠΠ»ΠΈΠ±ΠΎΠΊΡ Π·Π½Π°Π½Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΠΊΠΈ, ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΡΠ° ΡΠ΅ΠΎΡΡΡ ΠΉΠΌΠΎΠ²ΡΡΠ½ΠΎΡΡΠ΅ΠΉ;
β ΠΠ°Π²ΠΈΡΠΊΠΈ ΠΏΡΠΎΠ³ΡΠ°ΠΌΡΠ²Π°Π½Π½Ρ Π½Π° Python;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Π±ΡΠ±Π»ΡΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ: Pandas, Numpy, Scipy, Scikit-learn, Ρ ΡΠ΄.;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ ΡΠ· SQL;
β ΠΠ»ΠΈΠ±ΠΎΠΊΠ΅ ΡΠΎΠ·ΡΠΌΡΠ½Π½Ρ ΠΊΠ»Π°ΡΠΈΡΠ½ΠΈΡ ML Π°Π»Π³ΠΎΡΠΈΡΠΌΡΠ²: Clustering, Logistic Regression, Decision Trees, Random Forest, Boostings.ΠΠ΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ Π±ΡΠ΄Π΅ Π²Π΅Π»ΠΈΠΊΠΎΡ ΠΏΠ΅ΡΠ΅Π²Π°Π³ΠΎΡ:
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β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ Π· Time Series modeling;
β ΠΠΎΡΠ²ΡΠ΄ ΡΠΎΠ±ΠΎΡΠΈ DL framework: TensorFlow, PyTorch (+ CUDA);
β ΠΠΎΡΠ²ΡΠ΄ Π· GCP cloud: BigQuery, Cloud Functions.
Π’ΠΎΠ±Ρ ΡΠΎΡΠ½ΠΎ Π΄ΠΎ Π½Π°Ρ, ΡΠΊΡΠΎ ΡΠΈ:
β ΠΠΎΠ»ΠΎΠ΄ΡΡΡ Π²ΡΠ΄ΠΌΡΠ½Π½ΠΈΠΌΠΈ Π°Π½Π°Π»ΡΡΠΈΡΠ½ΠΈΠΌΠΈ Π½Π°Π²ΠΈΡΠΊΠ°ΠΌΠΈ ΡΠ° ΠΊΡΠΈΡΠΈΡΠ½ΠΈΠΌ ΠΌΠΈΡΠ»Π΅Π½Π½ΡΠΌ;
β ΠΠ°ΡΡ ΡΠΈΠ»ΡΠ½Ρ ΠΎΡΠ³Π°Π½ΡΠ·Π°ΡΠΎΡΡΡΠΊΡ Π·Π΄ΡΠ±Π½ΠΎΡΡΡ;
β Π£Π²Π°ΠΆΠ½ΠΈΠΉ Π΄ΠΎ Π΄Π΅ΡΠ°Π»Π΅ΠΉ, Π° ΡΠ°ΠΊΠΎΠΆ ΠΌΠ°ΡΡ Π½Π°Π²ΠΈΡΠΊΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ Π²ΠΈΡΡΡΠ΅Π½Π½Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌ, ΡΠΏΡΠ°Π²Π»ΡΠ½Π½Ρ ΡΠ°ΡΠΎΠΌ Ρ Π»ΠΎΠ³ΡΠΊΠΈ;
β ΠΠ°ΡΡ ΠΆΠ°Π³Ρ Π΄ΠΎ ΡΡΠ·Π½ΠΎΠ³ΠΎ ΡΠΎΠ΄Ρ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Ρ ΡΠ° ΡΠ°ΠΌΠΎΠ²Π΄ΠΎΡΠΊΠΎΠ½Π°Π»Π΅Π½Π½Ρ;
β ΠΠΎΠ»ΠΎΠ΄ΡΡΡ Π½Π°Π²ΠΈΡΠΊΠ°ΠΌΠΈ Π΅ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡ ΠΊΠΎΠΌΡΠ½ΡΠΊΠ°ΡΡΡ, Π·Π΄Π°ΡΠ½ΠΈΠΉ ΡΡΡΠΊΠΎ ΡΠ° Π»Π°ΠΊΠΎΠ½ΡΡΠ½ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΠΈ Π΄Π°Π½Ρ ΠΊΠΎΠΌΠ°Π½Π΄Π°ΠΌ ΡΠ° Π·Π°ΡΡΠΊΠ°Π²Π»Π΅Π½ΠΈΠΌ ΡΡΠΎΡΠΎΠ½Π°ΠΌ.
ΠΠΈ ΠΏΡΠΎΠΏΠΎΠ½ΡΡΠΌΠΎ:
β ΠΡΠ΄ΡΡΡΠ½ΡΡΡΡ Π±ΡΡΠΎΠΊΡΠ°ΡΡΡ Π² ΠΏΡΠΎΡΠ΅ΡΠ°Ρ ΠΏΡΠΈΠΉΠ½ΡΡΡΡ ΡΡΡΠ΅Π½Ρ Ρ ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π±Π΅Π·ΠΏΠΎΡΠ΅ΡΠ΅Π΄Π½ΡΠΎ Π²ΠΏΠ»ΠΈΠ²Π°ΡΠΈ Π½Π° ΠΏΡΠΎΠ΄ΡΠΊΡ/ΠΏΡΠΎΡΠΊΡ;
β ΠΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ Π½Π°Π²ΡΠ°ΡΠΈΡΡ β Π°Π±ΠΎ Π½Π°Π²ΡΠ°ΡΠΈ (ΠΌΠ°ΡΠΌΠΎ ΠΏΡΠΎΡΠΊΡΠΈ Π· ΡΠ½ΡΠ΅ΡΠ½Π°ΡΡΡΠΈ ΡΠ° ΠΌΠ΅Π½ΡΠΎΡΡΡΠ²Π°);
β Π Π΅Π°Π»ΡΠ·Π°ΡΡΡ ΡΠ΄Π΅ΠΉ ΡΠ΅ΡΠ΅Π· Π²Π»Π°ΡΠ½Ρ ΠΏΡΠΎΡΠΊΡΠΈ;
β ΠΠ΅ Π±ΡΠΉΡΠ΅ΡΡ Π΅ΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡΠ²Π°ΡΠΈ! ΠΡΠΎΠΏΠΎΠ½ΡΠΉΡΠ΅ ΡΠ° ΠΎΠ²Π½Π΅ΡΡΡΡ ΠΏΡΠΎΡΠ΅Ρ ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ;
β ΠΡΠ΄ΡΡΠΈΠΌΡΡΡΠ΅ ΡΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΈΡΠ΅ ΡΠ° ΠΊΠΎΠΌΠ°Π½Π΄Π°, ΡΠ· ΡΠΊΠΎΡ ΠΌΠΎΠΆΠ½Π° ΡΠΎΠ±ΠΈΡΠΈ Π΄ΡΠΉΡΠ½ΠΎ ΠΊΡΡΡΡ ΠΏΡΠΎΠ΄ΡΠΊΡΠΈ, ΡΠΎ Π·ΠΌΡΠ½ΡΡΡΡ ΡΠΈΠ½ΠΎΠΊ;
β ΠΠ°ΡΠΏΠ»Π°ΡΡ ΡΡΠ²Π½Ρ IT-/iGaming-ΡΠΈΠ½ΠΊΡ ΡΠ° ΠΏΠΎΠ²Π½ΠΈΠΉ ΡΠΎΡΠΏΠ°ΠΊΠ΅Ρ (ΠΌΠ΅Π΄ΠΈΡΠ½Π° ΡΡΡΠ°Ρ ΠΎΠ²ΠΊΠ°, ΠΊΠΎΠ½ΡΡΠ»ΡΡΠ°ΡΡΡ ΡΠ΅ΡΠ°ΠΏΠ΅Π²ΡΠ° Π² ΠΎΡΡΡΡ, ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ ΡΠΏΠΎΡΡΠ·Π°Π»Ρ, ΠΊΠΎΠΌΠΏΠ΅Π½ΡΠ°ΡΡΡ Π²Π°ΡΡΠΎΡΡΡ Π»Π°Π½ΡΡΠ² Π· Π΄ΠΎΡΡΠ°Π²ΠΊΠΎΡ ΡΠΎΡΠΎ);
β ΠΡΡΡΠ½ΠΈΠΉ ΠΎΡΡΡ Ρ ΡΠ΅Π½ΡΡΡ ΠΠΈΡΠ²Π° (ΠΏΡΡΠΊΠΈ Π·Ρ ΠΠ²ΡΡΠΈΠ½Π΅ΡΡΠΊΠΎΡ/ΠΠΈΠ±ΡΠ΄ΡΡΠΊΠΎΡ) ΡΠ· Π·Π΅Π»Π΅Π½ΠΎΡ ΠΏΠ°Π½ΠΎΡΠ°ΠΌΠ½ΠΎΡ ΡΠ΅ΡΠ°ΡΠΎΡ. ΠΡΠΎΠ±Π»Π΅ΠΌΠ° Π±Π»Π΅ΠΊΠ°ΡΡΡΠ² Π²ΠΈΡΡΡΠ΅Π½Π° Π½Π° 100%;
β ΠΡΠ΄ΠΏΡΡΡΠΊΠ° - Ρ ΡΠ΅Π±Π΅ Π±ΡΠ΄Π΅ ΠΎΠΏΠ»Π°ΡΡΠ²Π°Π½Π° Π²ΡΠ΄ΠΏΡΡΡΠΊΠΈ ΡΠ° Π΅ΠΊΡΡΡΠ°Π²ΠΈΡ ΡΠ΄Π½Ρ - Π² Π΅ΠΊΡΡΡΠ°Π΄Π½Ρ Π½Π°Π΄Π°ΡΠΌΠΎ Π΅ΠΊΡΡΡΠ°Π²ΠΈΡ ΡΠ΄Π½Ρ Π½Π°: ΠΎΠ΄ΡΡΠΆΠ΅Π½Π½Ρ, Π½Π°ΡΠΎΠ΄ΠΆΠ΅Π½Π½Ρ Π΄ΠΈΡΠΈΠ½ΠΈ, Π½Π΅ΠΏΠ΅ΡΠ΅Π΄Π±Π°ΡΡΠ²Π°Π½Ρ ΠΏΠΎΠ΄ΡΡ ΡΠ° ΡΠ½ΡΠ΅;
β ΠΠΎΠ½ΡΡ Π·Π° ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΡΡ - ΠΠΈ Π·Π°Π²ΠΆΠ΄ΠΈ ΡΠ°Π΄ΡΡΠΌΠΎ ΡΠ° ΡΡΠ½ΡΡΠΌΠΎ ΡΠ΅, ΡΠΎ ΡΡΠΌΠΌΠ΅ΠΉΡΠΈ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄ΡΡΡΡ ΡΠ²ΠΎΡΡ Π΄ΡΡΠ·ΡΠ², ΡΠΎΠΌΡ Π΄ΠΎ ΠΏΠ»ΡΡΡΠ² ΡΠΎΠ±ΠΎΡΠΈ Π· ΠΏΠ΅ΡΠ΅Π²ΡΡΠ΅Π½ΠΎΡ ΡΠ° Π½Π°Π΄ΡΠΉΠ½ΠΎΡ Π»ΡΠ΄ΠΈΠ½ΠΎΡ ΠΌΠΈ Π΄ΠΎΠ΄Π°ΡΠΌΠΎ Π±ΠΎΠ½ΡΡ;
β Π Π΅Π»ΠΎΠΊΠ΅ΠΉΡ - Π·ΠΌΡΠ½Π° ΠΌΡΡΡΠ° ΠΏΡΠΎΠΆΠΈΠ²Π°Π½Π½Ρ Π·Π°Π²ΠΆΠ΄ΠΈ ΡΠΏΠΎΠ½ΡΠΊΠ°Ρ Π΄ΠΎ Π΄ΠΎΠ΄Π°ΡΠΊΠΎΠ²ΠΈΡ Π²ΠΈΡΡΠ°Ρ, Π° Π½Π°Ρ Π±ΠΎΠ½ΡΡ Π΄ΠΎΠΏΠΎΠΌΠ°Π³Π°Ρ ΠΏΡΠΎΠΉΡΠΈ ΡΠ΅ΠΉ ΠΏΠ΅ΡΡΠΎΠ΄ Π±Π΅Π· Π·Π°ΠΉΠ²ΠΈΡ ΡΡΡΠ΅ΡΡΠ².
Π―ΠΊΡΠΎ ΡΠΈ ΡΡΠΊΠ°ΡΡ Π΄Π»Ρ ΡΠ΅Π±Π΅ ΡΡΠ°Π±ΡΠ»ΡΠ½Ρ ΠΊΠΎΠΌΠΏΠ°Π½ΡΡ Π· ΠΊΠ»Π°ΡΠ½ΠΈΠΌΠΈ Π»ΡΠ΄ΡΠΌΠΈ ΡΠ° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΡΠΎΡΡΡ - ΡΠΎΠ±Ρ Π΄ΠΎ Π½Π°Ρ! ΠΡΠ΄ΠΏΡΠ°Π²Π»ΡΠΉ ΡΠ΅Π·ΡΠΌΠ΅!