Jobs Data Science

Responsibilities

Model Fine-Tuning and Deployment:

Fine-tune pre-trained models (e.g., BERT, GPT) for specific tasks and deploy them using Amazon SageMaker and Bedrock.

RAG Workflows:

Establish Retrieval-Augmented Generation (RAG) workflows that leverage knowledge bases built on Kendra or OpenSearch. This includes integrating various data sources, such as corporate documents, inspection checklists, and real-time external data feeds.

MLOps Integration:

The project includes a comprehensive MLOps framework to manage the end-to-end lifecycle of machine learning models. This includes continuous integration and delivery (CI/CD) pipelines for model training, versioning, deployment, and monitoring. Automated workflows ensure that models are kept up-to-date with the latest data and are optimized for performance in production environments.

Scalable and Customizable Solutions:

Ensure that both the template and ingestion pipelines are scalable, allowing for adjustments to meet specific customer needs and environments. This involves setting up RAG workflows, knowledge bases using Kendra/OpenSearch, and seamless integration with customer data sources.

End-to-End Workflow Automation:

Automate the end-to-end process from user input to response generation, ensuring that the solution leverages AWS services like Bedrock Agents, CloudWatch, and QuickSight for real-time monitoring and analytics.

Advanced Monitoring and Analytics:

Integrated with AWS CloudWatch, QuickSight, and other monitoring tools, the accelerator provides real-time insights into performance metrics, user interactions, and system health. This allows for continuous optimization of service delivery and rapid identification of any issues.

Model Monitoring and Maintenance:

Implement model monitoring to track performance metrics and trigger retraining as necessary.

Collaboration:

Work closely with data engineers and DevOps engineers to ensure seamless integration of models into the production pipeline.

Documentation:

Document model development processes, deployment procedures, and monitoring setups for knowledge sharing and future reference.

Must-Have Skills

Machine Learning: Strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

MLOps Tools: Proficiency with Amazon SageMaker for model training, deployment, and monitoring.

Document processing: Experience with document processing for Word, PDF, images.

OCR: Experience with OCR tools like Tesseract / AWS Textract (preferred)

Programming: Proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-Learn.

Model Deployment: Experience with deploying and managing machine learning models in production environments.

Version Control: Familiarity with version control systems like Git.

Automation: Experience with automating ML workflows using tools like AWS Step Functions or Apache Airflow.

Agile Methodologies: Experience working in Agile environments using tools like Jira and Confluence.

Nice-to-Have Skills

LLM: Experience with LLM / GenAI models, LLM Services (Bedrock or OpenAI), LLM abstraction like (Dify, Langchain, FlowiseAI), agent frameworks, rag.

Deep Learning: Experience with deep learning models and techniques.

Data Engineering: Basic understanding of data pipelines and ETL processes.

Containerization: Experience with Docker and Kubernetes (EKS).

Serverless Architectures: Experience with AWS Lambda and Step Functions.

Rule engine frameworks: Like Drools or similar

If you are a motivated individual with a passion for ML and a desire to contribute to a dynamic team environment, we encourage you to apply for this exciting opportunity. Join us in shaping the future of infrastructure and driving innovation in software delivery processes.

We are looking for an experienced Data Scientist for a full-time job to join our team.


Requirements:
- 2+ years of experience as a data scientist
- BSc in Mathematics, Statistics, Computer Science, Economics, or another related field.
- Experience in using Python & SQL
- Experience with Airflow and GCP
- Experience with Git & CI/CD
- Upper Intermediate English — written and spoken
- Ability to design creative solutions for complex requirements
- Ability to learn and lead projects independently, and to work with minimal supervision with customers (tech & business)

Responsibilities:
- Conduct independent research, including defining research problems, creating research plans, designing experiments, developing algorithms, implementing code, and performing comprehensive comparisons against existing benchmarks;
- Clearly communicate your research findings to both technical and non-technical audiences
- Work on various data sources and apply sophisticated feature engineering capabilities
- Bring and use business knowledge
- Build and manage technical relationships with customers and partners.

We offer:
- remote time job, B2B contract
- 12 sick leaves and 18 paid vacation business days per year
- Comfortable work conditions (including MacBook Pro and Dell monitor on each workplace)
- Smart environment
- Interesting projects from renowned clients
- Flexible work schedule
- Competitive salary according to the qualifications
- Guaranteed full workload during the term of the contract
 

We are looking for a Computer Vision Engineer with a solid foundation in classical computer vision and hands-on experience implementing low-level CV algorithms. The ideal candidate will have exposure to SLAM, Visual-Inertial Odometry (VIO), and sensor fusion, with responsibilities adjusted according to experience level.

Responsibilities: 

• Implement and optimize low-level computer vision algorithms under guidance
• Contribute to modules related to feature extraction, tracking, matching, and geometric processing
• Assist in the integration and testing of SLAM/VIO components
• Perform experiments, analyze results, and help prepare reports
• Work closely with senior engineers to explore and apply recent CV research.

Required Qualifications:

• 1+ years of hands-on experience with classical computer vision
• Basic understanding of geometric computer vision principles
• Experience implementing low-level CV algorithms (e.g., filtering, feature detection, camera models)
• Familiarity with SLAM or VIO concepts
• Working knowledge of C++ and Linux
• Ability to learn quickly and navigate recent CV researc
• Degree or ongoing studies in a STEM field.

Nice to Have:

• Experience with Python
• Knowledge of neural networks and CV libraries/frameworks (OpenCV, NumPy, PyTorch, ONNX, Eigen, etc.)
• Experience with sensor fusion techniques
• Familiarity with embedded or real-time systems

About the Company

 European deep-tech company developing innovative AI solutions at the intersection of sensor technology, data science, and machine learning. Our products transform complex data from physical sensors into accurate analytical insights for real-time decision-making across multiple industries, including environmental monitoring, product quality control, and applied research.

The team works at the intersection of artificial intelligence, signal processing, and experimental analytics, building advanced models for pattern recognition and predictive analysis based on complex datasets.

Responsibilities

-Process, clean, and validate data for building ML/DL models.

-Design, implement, and optimize Machine Learning and Deep earning models.

-Conduct experiments including training, testing, fine-tuning, and hyperparameter optimization.

-Analyze results and present insights clearly to the team.

-Improve data collection and preprocessing pipelines.

-Collaborate closely with the ML Lead and engineering team.

-Participate in selecting modeling approaches and experimental strategies.

-Work with sensor-generated and multidimensional datasets.

Requirements

-5+ years of experience in Data Science / Machine Learning / Deep Learning.

-Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related field.

-Commercial experience building and deploying ML/DL models.

Strong understanding of algorithms:

-Classification and regression

-Clustering

-CNN and fully connected neural networks

-Time-series analysis

-Experience working with databases (SQL or solutions from Oracle Corporation).

Practical experience with:

-Python

-PyTorch or TensorFlow

-scikit-learn

-NumPy, Pandas

-Experience training models in cloud environments (e.g., Amazon Web Services).

-Experience working in a Linux environment.

-Experience using Git.

-English level sufficient for technical communication.

Nice to Have

-Experience working with sensor or experimental datasets.

-Understanding of signal processing or pattern recognition.

-Experience building experimental ML pipelines. 

Requirements:

• 5+ experience in applying data science and machine learning techniques
• Python programming using classic scientific stack (numpy, pandas, scipy, scikit-learn, pytorch, lightgbm, matplotlib or any other visualization library)
• Understanding of classic machine learning algorithms and theory (supervised/unsupervised learning techniques, metrics and visualizations for interpretation of model performance, time-series analysis, anomaly detections, recommendation models, hyperparameter optimization)
• NLP (NLP techniques, GenAI, LLM finetuning)
• Statistics and optimization basic understanding
• Data manipulation and cleaning skills (fill missing entries / filter dataframes / apply certain changes to columns)
• Prior experience with Python web frameworks would be great, but not critical (FastAPI, Flask or anything else)
• Dockerization technologies
• Cloud solutions (any of AWS, GCP, Azure)
• Understanding of prompt engineering or experience with building scripts or applications around LLMs
• Proficient with SQL (Postgres, Snowflake), NoSQL (ElasticSearch, Mongo(optional))
• Monitoring services
   
• Responsibilities:
  Work on time series forecasting problems with real, complex data. Implement features, interpret their importance, train models and incorporate them in the production pipeline.
• Work on LLM-based solutions, which use techniques like Contextual RAG (Retrieval Augmented Generation), LLM as Judge, semantic search, chatbot services, etc.
• Create anomaly detection models and clustering methods for non-stationary, “dirty”, semi-organized data

We’re opening applications for a 3-month intensive internship where participants will work on real company projects under the guidance of Senior Data Scientists with years of hands-on experience.

PARTICIPANTS

We’re looking for motivated and ambitious individuals eager to sharpen their Data Science skills.

To participate in the internship program, you need:

- Strong Python knowledge;
- Good skills in programming and algorithms;
- Good background in Math;
- Hand on experience in Machine Learning/Computer Vision/NLP (including pet projects);
- Knowledge of base ML libraries in Python;
- Strong knowledge of Git;
- English level Intermediate (B2).

STRUCTURE / MENTOR

- Kick-off Session — meet your mentor, fellow interns, and dive into the Skill Evaluation stage.
- Project-Based Learning — solve real Data Science tasks, collaborate on projects, and gain experience in a structured learning environment.
- Continuous Mentorship — benefit from ongoing guidance, code reviews, and feedback sessions designed to accelerate your growth.
- Every intern will work under the guidance of Senior Data Scientists with 5+ years of industry experience.

RESULTS

- Practical experience in AI, Machine Learning, Deep Learning, NLP, and Computer Vision.
- The chance to work on impactful, production-level projects.
- Opportunity to receive a full-time job offer at WINSTARS.AI upon successful completion of the internship.

Fozzy Group is one of the largest trade industrial groups in Ukraine and one of the leading Ukrainian retailers, with over 700 outlets all around the country. It is also engaged in e-commerce, food processing & production, agricultural business, parcel delivery, logistics and banking.
 

The Foodtech team is looking for a Data Scientist to develop the Operational Analytics function for a fast growing food delivery business. In this role, you will focus on time series forecasting, regression modeling, simulation modeling, and end-to-end machine learning pipelines to support resource planning and operational decision-making.

You will be responsible for developing simulation-based models that serve as a foundation for a digital twin of operational processes, enabling scenario analysis, stress testing, and what-if simulations for capacity planning and operational optimization.

You will work closely with product, engineering, and operations teams to transform data into measurable business impact through production-ready ML and simulation solutions.
 

Job Responsibilities:

• Develop and implement time series forecasting models for resource planning (demand, capacity, couriers, delivery slots, operational load);
• Build regression and machine learning models to explain key drivers and support operational decisions;
• Apply a wide range of time series approaches from classical models (SARIMA, ETS, Prophet) and ML models (GB) to modern Deep Learning models (LSTM, Temporal CNNs, Transformers for TS);
• Design, build, and maintain end-to-end automated ML pipelines, deploy and operate models in production using AWS SageMaker;
• Orchestrate training and inference workflows with Apache Airflow;
• Analyze large-scale operational datasets and convert results into insights, forecasts, and actionable recommendations;
• Collaborate with product managers, engineers, and operations teams to define business problems and validate analytical solutions;
• Monitor model performance, forecast stability, and business impact over time.
   

Requirements:

• Bachelor’s Degree in Mathematics / Engineering / Computer Sciences / Quantitative Economics / Econometrics;
• Strong mathematical background in Linear algebra, Probability, Statistics & Optimization Techniques;
• At least 2 years working experience on Data Science;
• Experience of the full cycle of model implementation (data collection, model training and evaluation, model deployment and monitoring);
• Ability to work independently, proactively, and to decompose complex problems into actionable tasks.
   

Skills:

• Strong proficiency in Python with solid application of object-oriented programming (OOP) principles (modular design, reusable components, maintainable code);
• Solid experience in time series forecasting and regression modeling;
• Practical knowledge of:o Classical and ML forecasting techniques;
  o Statistical methods (hypothesis testing, confidence intervals, A/B testing);
• Advanced SQL skills (window functions, complex queries);
• Experience building automated ML pipelines;
• Understanding of MLOps principles (model versioning, monitoring, CI/CD for ML).
   

Nice to have:

• Hands-on experience with AWS SageMaker (training jobs, endpoints, model registry);
• Experience with Apache Airflow for data and ML workflow orchestration;
• Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau);
• Experience working with large-scale production data systems.
   

What We Offer:

• Competitive salary;
• Professional & personal development opportunities;
• Being part of dynamic team of young & ambitious professionals;
• Corporate discounts for sport clubs and language courses;
• Medical insurance package.

Almus is looking for a Data Scientist to join our Analytics team and build production-grade machine learning models that directly impact marketing and business performance.

You will work on end-to-end ML solutions, from data and features to deployment and monitoring, focusing on improving LTV prediction quality, optimizing ML-driven costs, and driving key metrics such as LTV, ROAS, retention, and CAC. This is an individual contributor role with strong ownership, close collaboration with Marketing, Product, and Data teams, and a clear focus on real business impact.

Apply to join Almus and take ownership of high-impact data initiatives!

Responsibilities

• Design, develop, and deploy machine learning models to production
• Improve product and business decision-making through data-driven approaches
• Build and evolve end-to-end ML pipelines (data → features → model → inference → monitoring)
• Drive measurable impact on key product and commercial metrics
• Standardize ML approaches within the team (best practices, documentation, reproducibility)
• Provide technical input to the architecture of analytics and ML infrastructure
• Develop and deploy models that drive growth in LTV, ROAS, retention, and CAC
• Influence performance and lifecycle marketing strategy
• Act as a domain expert and collaborate closely with Marketing, Product, and Data Engineering teams

What We Look For

• 3+ years of experience as a Data Scientist / ML Engineer
• Experience working with mobile subscription-based products
• Strong Python skills (production-level code)
• Solid knowledge of classical machine learning algorithms and practical experience applying them
• Experience with feature engineering, model evaluation, and bias–variance trade-offs
• Hands-on experience with marketing models such as LTV, churn, cohort, and funnel modeling
• Experience with attribution, incrementality, and uplift modeling
• Strong SQL skills and experience working with analytical datasets
• Experience with production ML systems and A/B testing

• English level: Intermediate+

   

Nice to have

• Experience with BigQuery
• MLOps experience (Docker, CI/CD, model registres)
• Experience working with performance marketing data (Meta, Google Ads, Adjust)
• Knowledge of causal inference

• Experience with AutoML and Bayesian models

   

We Offer

• Exciting challenges and growth prospects together with an international company
• High decision-making speed and diverse projects
• Flexibility in approaches, no processes for the sake of processes
• Effective and friendly communication at any level
• Highly competitive compensation package that recognizes your expertise and experience, Performance Review practice to exchange feedback and discuss terms of cooperation
• Flexible schedule, opportunity to work in a stylish and comfortable office or remotely
• Respect for work-life balance (holidays, sick days - of course)
• Bright corporate events and gifts for employees
• Additional medical insurance
• Compensation for specialized training and conference attendance
• Restaurant lunches at the company's expense for those working in the office, endless supplies of delicious food all year round
   

We are looking for a Senior Data Scientist who can deliver end-to-end solutions, communicate both technical and business aspects to clients, and take ownership of production outcomes — helping Dastellar fulfill its mission to build custom AI/ML/Data Science solutions for businesses.

General Requirements:

• 5+ years in ML/Data Science.
• Strong experience with classical ML: sklearn, XGBoost/LightGBM, time-series models (ARIMA/Prophet/ETS or deep models), and recommender systems.
• Python 3.8+ proficiency; solid Pandas/NumPy; production-level experience with PyTorch or TensorFlow.
• Experience deploying ML models (Docker, CI/CD); basic cloud skills (AWS/GCP/Azure) preferred.
• Ability to communicate technical concepts to clients and translate risks into business impact.
• Autonomous and proactive; history of leading end-to-end tasks.
• English: B2+.
   

Nice-to-have / Growth Opportunities:

• LLM/GenAI experience: fine-tuning GPT/LLaMA/Hugging Face, building RAG/embedding pipelines, LangChain.
• Vector DBs: FAISS, Qdrant, Pinecone.
• MLOps basics: MLflow, W&B, CI/CD pipelines.
• Big Data: Spark, advanced SQL optimization.
• Domain expertise: energy or retail.
   

Main Responsibilities:

• Build classical ML models (classification, regression, clustering, recommendation, time-series).
• Data cleaning, validation, feature engineering, ETL/ELT pipelines.
• Design and deploy ML/GenAI solutions from POC to production.
• Integrate models with infrastructure (Docker, CI/CD; optionally Kubernetes/serverless).
• Monitor model performance and system health (metrics, data drift, alerts).
• Document solutions, create client presentations, and conduct R&D experiments.
• Maintain code quality: tests, reviews, reproducibility.
   

Soft Skills:

• Responsibility & ownership: end-to-end project delivery, meeting deadlines, post-release SLAs.
• Proactivity: proposing and implementing process or architecture improvements.
• Autonomy & problem-solving: translating ambiguous business goals into MVP roadmaps.
• Critical thinking: identifying data/assumption biases and proposing mitigation strategies.
   

What We Offer:

• Flexible part-time / contract work.
• Fast decision-making and low bureaucracy.
• Influence over architecture and stack choices.
• Remote collaboration and flexible schedule.
• Paid R&D participation (by agreement).

We are looking for a Computer Vision Engineer with solid ML/DL experience to join our team on challenging projects.
We consider engineers at Middle and Senior levels - tasks and responsibilities will be adjusted accordingly. 

Required Qualifications:

• 3+ years of experience in computer vision
• High proficiency in Python
• Strong experience with deep learning and the PyTorch framework for computer vision tasks
• Research experience in computer vision with a focus on delivering results
• Ability to work independently or within a small team, with a strong sense of ownership over results

Nice to Have:

• Experience with deep learning solutions for detection, tracking, image retrieval, and segmentation tasks
• Understanding of geometric computer vision principles
• Model optimization: quantization, pruning, neural network compilers
• Hands-on experience implementing low-level CV algorithms
• Experience with sensor fusion (IMU, magnetometer, GNSS, camera)
• Knowledge of C++

Our mission is to make learning technology accessible, easy and fun for users across the world! We are creating next-generation education platform for learning software development and intent to become a leading player in that market.

Are you interested in participating in the development of a platform that will be used by millions of users around the world? Do you want to help thousands of people enter the world of programming, data analysis, machine learning? Do you want to work in a team where your opinions and ideas really matter? If your answer to all the questions is yes, it's a match!
Currently we’re looking for a full-time Course Creator!

Responsibilities:

- Designing and creating various types of educational materials
- Monitoring the latest technological developments to quickly create new and, most importantly, relevant materials
- Collecting and analyzing feedback on developed materials
- Proposing and managing innovative ideas to improve not only the content team's performance but also the entire project
- In addition to the mandatory duties, there is the opportunity to engage in additional areas to some extent—like analytics, delve into content for social networks, or work on animations for courses, etc.

Requirements:

- Passion for educating others (experience as a tutor or teaching assistant is a plus)
- It is very important to have a main profile in a technical discipline - mathematics, computer science, or other related fields
- Academic background in mathematics, computer science, or other related fields. We are open to candidates with unfinished degrees (currently in progress)
- Video editing skills are also valued as the platform is moving towards a video format
- Creativity, artistic perspective
- Strong analytical and critical thinking
- English upper intermediate or higher;
- Ability to explain complex concepts in simple language;
- Thorough attention to detail.

What do we offer:

- Work within an ambitious team on a socially impactful education product.

- 20 working days of paid vacation per year, plus public holidays.

- An office with a reliable shelter, generators, satellite internet, and other amenities.

- Access to our corporate knowledge base and professional communities.

- Compensation for English language learning, external training, and courses.

- Medical insurance and full sick leave compensation.

- Company doctor and massage in the office.

- Sports activities: running, yoga, boxing, and more.

Are you passionate about turning complex data into actionable insights? Join us as a Strong Middle/Senior Data Scientist and help shape cutting-edge AdTech solutions that reach millions of users worldwide. 
 

We at Sigma Software bring together talented professionals to deliver high-load, data-driven platforms for global clients. Here, your expertise will be valued, your voice heard, and your career empowered. Why join us? You’ll work with modern technologies, collaborate with top-tier teams, and contribute to projects that make a real impact in the marketing and advertising industry.
 

Customer
Our customer is an international technology company specializing in high-load platforms for data processing and analytics. Their core product enables businesses to manage massive datasets, create predictive models, and extract actionable insights. Operating globally, they serve clients primarily in marketing and advertising, leveraging microservices architecture and cloud-based solutions.
 

Project
The project focuses on designing and implementing advanced machine learning models, building scalable data pipelines, and delivering analytical solutions for high-volume datasets. The goal is to improve product performance, enable better targeting and personalization in AdTech, and ensure model accuracy and reliability.
 

Responsibilities:

• Design, develop, and deploy machine learning models to solve complex business problems in the AdTech domain
• Analyze large datasets to generate actionable insights and improve product performance
• Build and maintain scalable data pipelines using big data tools and frameworks
• Perform data preprocessing, cleaning, feature engineering, and model evaluation
• Collaborate with cross-functional teams, including engineers, analysts, and product managers
• Ensure model accuracy, reliability, and scalability through rigorous testing and validation
• Stay updated on emerging data science techniques, tools, and best practices
• Contribute to team discussions, process improvements, and knowledge sharing

Requirements  

• At least 4 years  of experience applying machine learning, statistical modeling, and data mining techniques to real-world business problems
• Strong programming skills in Python and/or R
• Solid understanding of machine learning algorithms, their applications, and statistical principles
• Experience building and productionizing data pipelines and ML models in cloud environments, preferably AWS
• Hands-on experience working with large-scale data using PySpark or RSpark
• Proficient in SQL and experienced with Excel for data analysis and reporting
• Ability to measure the business impact of ML models using proper evaluation frameworks
• Familiarity with version control systems and agile development practices
• At least Upper-Intermediate level of English  

Personal profile 

• Excellent communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders 
• Strong problem-solving and decision-making skills, with a focus on driving results and meeting deadlines 
• Self-motivated, adaptable, and eager to learn new technologies and frameworks 

Our Client is a cybersecurity software company that specializes in network and cloud security policy management and automation. It's platform helps large enterprises visualize, automate, and enforce consistent security policies across complex hybrid environments — including firewalls, cloud services, SASE, SD-WAN, and microsegmentation — while ensuring compliance and reducing risk.

Position Description:

This role combines excellent technical skills, driving multidisciplinary Data Science initiatives within complex, security policy management systems. The role drives the development of innovative, production-grade AI capabilities - including security AI agents and advanced machine learning models built on complex security data. Original thinking, deep technical rigor, intellectual agility, and exceptional problem-solving are essential.

Key Responsibilities

• Develop Data Science initiatives from problem definition through validation, production deployment, and monitoring of AI systems
• Develop security-focused AI agents and reasoning systems
• Develop advanced ML capabilities, including predictive modeling, anomaly detection, and classification
• Adapt and fine-tune LLM technologies for domain-specific security use cases

Position Requirements:

• M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative discipline
• At least 3 years of hands-on Data Science experience, delivering end-to-end solutions into production environments
• Deep understanding of machine learning theory and practical model behavior
• Expertise in Python and the modern Data Science ecosystem (NumPy, Pandas, Scikit-learn, PyTorch / TensorFlow, etc.)
• Solid understanding of LLM architectures and fine-tuning methodologies
• Excellent interpersonal skills and proven ability to work within multidisciplinary product teams
• Strong analytical rigor and structured problem-solving capability

About The Role

As a Senior/Lead Software Engineer in our Machine Learning department, you will take ownership of high-impact initiatives that drive product quality, monetization, and user experience. You’ll apply your expertise in Python, data analysis, and modern AI tools to build, deploy, and optimize ML solutions end-to-end. This is a hands-on technical role that blends data science and engineering to deliver measurable business outcomes. You’ll collaborate closely with cross-functional stakeholders and contribute to evolving areas such as LLMs, Generative AI, and ML infrastructure. The role offers exposure to complex data challenges and the opportunity to shape Pearl’s AI-driven capabilities.

What You’ll Do

• Build, train, and deploy ML models that improve product performance and monetization.
• Collaborate with data, product, and engineering teams across multiple internal initiatives.
• Design and implement data pipelines for extraction, transformation, and analysis.
• Apply statistical and machine learning techniques to real-world business problems.
• Work on projects involving LLMs and Generative AI technologies.
• Analyze large datasets to identify trends, insights, and optimization opportunities.
• Integrate ML models into production systems and monitor their performance.
• Communicate findings and recommendations clearly to technical and non-technical stakeholders.
• Experiment with emerging AI tools and methodologies to enhance existing workflows.
• Ensure data quality, reliability, and scalability in all deliverables.

What We’re Looking For

• 3+ years of hands-on experience in ML Engineering or Data Science.
• 5-6+ years total experience in software or data engineering.
• Strong programming skills in Python.
• Proficiency in SQL for data querying and analysis.
• Solid understanding of data processing, analysis, and visualization.
• Experience with LLMs / Generative AI tools (e.g., OpenAI, Copilot, Cursor).
• Ability to build and deploy ML models end-to-end.
• Strong analytical and problem-solving mindset.
• Experience with .NET for integration work
• Upper-intermediate English proficiency (B2 or higher).
• Familiarity with Databricks or similar data platforms (nice-to-have).
• Exposure to AWS or other cloud-based ML environments (nice-to-have).

Why Join Us?

• High-Impact Data — Work with vast datasets generated by over a million daily visits, uncovering valuable insights.
• Collaborative Analytics Team — Join a team of 30+ skilled analysts who actively share knowledge and support each other.
• Cutting-Edge Tools & Techniques — Get hands-on experience with ML, Bayesian methods, and LLMs to solve real business challenges.
• Data-Driven Culture — Thrive in an environment where data informs every decision and drives continuous improvement.

Who We Are

At Bennett Data Science, we've been pioneering the use of predictive analytics and data science for over a decade for some of the biggest brands and retailers. We're at the top of our field because we focus on delivering actionable AI for our clients. Our deep experience and product-first attitude set us apart from other groups and gets us the business results our clients want.

Why You Should Work With Us

You'll be exposed to a wide range of clients who are at the cutting edge of innovation in their field and get to work on fascinating problems, supporting real products, with real data. We help lots of companies, from some of the largest companies in the world to small startups in Silicon Valley who are building the next big thing.

Expert Mentorship: Direct guidance from senior staff with 20+ years of applied ML experience

Competitive Compensation: Market-rate pay with performance upside

Fully Remote: Work from any location of your choice, on a flexible schedule

Real Impact: Your models go into production and serve real users

The Role

As a Senior Data Scientist, you will lead the design, development, and deployment of production machine learning models. You will work directly with stakeholders to understand their business problems, translate them into data science solutions, and deliver measurable impact. You will also mentor junior team members and drive technical excellence across engagements.

You'll own projects end-to-end, from exploratory analysis and model development through deployment, monitoring, and iteration alongside senior data engineers. This is a hands-on, individual contributor role at a senior level. Client-facing communication is part of the job.

Requirements

A successful candidate has 5+ years of experience in applied data science and machine learning, a strong statistical foundation, and demonstrates the following:

• Production ML experience: building, deploying, and maintaining models that serve real users at scale
• Strong Python skills including scikit-learn, pandas, NumPy, and at least one deep learning framework (PyTorch or TensorFlow)
• Proven experience building predictive scoring, classification, or ranking models deployed to production at scale
• Proficiency in SQL and comfort working with large-scale data warehouses (Redshift, Snowflake, BigQuery)
• Experience deploying RAG-based chat bots, including  how to reduce hallucinations and a strong understanding of the tradeoffs across various approaches
• Solid statistical foundation: hypothesis testing, distributions, probability, experimental design
• Experience communicating findings and model recommendations to non-technical stakeholders
• Comfort working independently across multiple projects simultaneously
• English proficiency at B2 or above (written and spoken)

Nice to Have

• Experience applying LLMs or transformer-based NLP for structured text classification, information extraction, or embedding-based retrieval
• Geospatial feature engineering — location-based statistics, spatial indexing, or OpenSearch for proximity scoring
• Experience with Vision-Language Models (VLMs) or satellite/aerial imagery analysis
• Familiarity with MLOps tooling — experiment tracking (MLflow, W&B), model registries, CI/CD for ML
• Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI)
• Exposure to utilities, energy, infrastructure, or enterprise SaaS domains
• Experience fine-tuning or adapting pre-trained models (LoRA, PEFT, or full fine-tune)

Role Expectations

• This is a production role. We build systems that run in the real world