Jobs Data Science

Most AI engineers build demos. You'll build the production system a client's business actually depends on.

Team: AI Delivery Practice 

Location: Remote (Ukraine/Europe) 

Employment: Full-time 

Reports to: Head of AI Delivery/Engineering, not Sales, not Customer Success

Travel: Typically 20–30%, concentrated around engagement kickoffs and go-live weeks, confirmed with you per engagement, not a surprise

The Job, Honestly

Most AI engineering jobs let you perfect a demo. This one doesn't. You get one client, one real opportunity, and six to twelve weeks to turn it into something running on live traffic — in their environment, on their data, with their security team watching. Then you do it again, for a different client, in a different industry, with a different set of constraints.

Success here isn't measured in closed tickets. It's measured in production adoption, business impact, and whether the client can run what you built without you standing over their shoulder.

If that sounds like the fun part of the job rather than the exhausting part, keep reading.

The AI Practice

You won't be the only person figuring this out. Delivery patterns each engineer builds — a guardrail design, an eval harness, a way of scoping a RAG project so it doesn't die in week 6 — get written down and reused by the next person on the next engagement. You're not starting from zero each time, and neither is the person after you.

Engagements span the kind of variety a single-product company can't offer: a RAG system on a client's live data one quarter, an agent with real write-access and real guardrails the next, a legacy enterprise stack under regulatory constraints after that.

What the Work Actually Looks Like

Discovery through production — not discovery through a slide deck

• You sit with the client's team — their repos, their data, their stand-ups — and find the AI opportunity worth betting six weeks on, not the one that looks best in a pitch

• You analyze their business processes, data landscape, and existing systems well enough to translate a business problem into a production-ready AI architecture

• You prototype it, validate it with real end users, and iterate on measurable outcomes before you've convinced yourself it's right

• You ship it — and you're still the one on call when it breaks in week two of production, not someone who inherits your code

• You capture what you learned so the next engagement, and the next engineer, start a little further ahead than you did

Before the contract is even signed

• Sales brings you into scoping calls, because "can this actually ship in 6–12 weeks" is an engineering judgment, not a sales one — and what you say shapes what gets sold

The actual engineering, once you're in

• Production LLM applications — agents, RAG, orchestration — built on whatever stack the client's environment actually runs on, not the stack that's most fun to write in

• Integrations into whatever the client already has: their APIs, identity provider, cloud, messaging systems, the legacy piece nobody wants to touch

• Reliability work that never shows up in a demo: tool-call guardrails, retries, fallbacks, human-in-the-loop checkpoints

• Eval pipelines, golden datasets, and observability — because the job isn't done at go-live, it's done when the client can run the thing on their own

• The unglamorous optimization work: latency, cost, security, maintainability — whatever the client's actual bottleneck turns out to be

Who Tends to Thrive Here

Less a checklist, more a description of the person who's already done a version of this job:

• You can talk in detail about a production AI system you shipped for an external customer — what they wanted at kickoff, what they wanted at launch, and why that changed. This matters more to us than any line on your CV

• 5+ years as a commercial software engineer, with strong fundamentals and the ability to pick up whatever stack a given engagement requires — we hire for engineering depth and adaptability, not a fixed programming language

• Fluent with at least one modern LLM platform (OpenAI, Anthropic, Gemini, Azure OpenAI, Bedrock), and comfortable in the RAG/vector/embeddings/context-engineering space without needing a primer

• You've built agents with something like LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen — and you've watched one misbehave in production, which taught you more than the framework docs did

• You can ship and run things on AWS/Azure/GCP with Docker, Kubernetes, and CI/CD — and you understand enough about IAM, auth, and networking to not be the reason the client's security team says no

• You can hold your own in a room with a skeptical enterprise stakeholder who's been burned by an AI vendor before — not by having all the answers, but by being straight about trade-offs

• Upper-Intermediate English or better, because a lot of this job is a conversation, not a pull request

Extra credit, not a requirement

• You've built eval frameworks, golden datasets, or regression tests for AI systems, or worked with LangSmith, Langfuse, Arize Phoenix, or MLflow

• You know your way around AI governance, guardrails, hallucination mitigation, or prompt injection — or MCP / A2A and where AI interoperability is heading

• You've touched Databricks, Azure AI Foundry, Bedrock, or Vertex AI at the platform level, not just the API level

• You've done consulting, solution architecture, or technical pre-sales before — or worked inside a regulated industry (healthcare, finance, telecom, government) and know why that changes everything

• You have a GitHub history, a conference talk, or a client-co-authored case study that shows this rather than just claiming it

Your First 90 Days

• Weeks 1–2: Embedded in your first client's environment, with a validated, highest-value use case identified jointly with their team — not handed to you

• Weeks 3–8: Architecture designed, prototype validated with real end users, production build underway

• Weeks 9–12: System live on real traffic, eval pipeline in place, documentation and handover ready for the client's own team to take over

What We Offer

• Competitive compensation with bonuses tied to successful production go-lives; B2B contract available

• Direct visibility into your impact — your code runs at the client, not sitting in a backlog

• No two engagements are the same: a fintech RAG project this quarter, a regulated-industry agent build next

• Certifications (Anthropic, OpenAI, AWS, GCP, Azure) and a professional development budget

• Health insurance, English classes, sports activities, and a culture that tells you the hard parts of the job up front — like we just did

If the six-to-twelve-week cycle, the client-hopping, and the "you're still holding the pager" ownership sound energizing rather than draining — this is probably your role. Send us your CV, and tell us about the production AI system you're proudest of shipping.

We are looking for a Quantitative Analyst to become the mathematical backbone of our Trading Core and Risk Tech squads. In a high-frequency trading environment processing billions in volume, success is defined by precision. 

In this role, you will design, backtest, and optimize the core mathematical models that drive our pricing engines, automated market-making algorithms, risk management frameworks (A/B/C-book optimization), and liquidation mechanics. You will sit at the intersection of advanced mathematics, data science, and high-performance software engineering, working directly with Core Product Managers and engineering teams to turn complex financial data into proprietary algorithmic advantages.

Responsibilities

• Research, design, and prototype quantitative models for pricing, risk management, and market making
• Build and maintain robust backtesting frameworks to validate the performance and safety of models before production deployment
• Write clear, comprehensive mathematical and algorithmic specifications for Backend Engineers (Trading Core squads)
• Cross functional with the R&D and Trading Ops teams
• Conduct post-incident deep dives (e.g., after major market gaps or liquidations) to identify algorithm performance gaps and optimize them

Requirements

• 3+ years of experience as a Quantitative Analyst / Researcher or Data Science
• Exceptional knowledge of probability theory, stochastic calculus, time-series analysis, and financial mathematics
• Advanced proficiency in Python (NumPy, Pandas, SciPy, SK-Learn, Statsmodels) for data analysis, modeling, and backtesting
• Experience with Machine Learning
• Deep understanding of market microstructure, order book dynamics, risk metrics (VaR, Expected Shortfall), and margin/liquidation mechanisms
• SQL skills and experience working with large-scale historical market data (tick data, order logs)
• Strong logical thinking, initiative, and well-developed communication skills

Will be a plus

• Experience in CFD, Crypto CEX, Prop Trading Firm, or Hedge Fund
• Degree (MSc or PhD preferred) in a highly quantitative field: Mathematics, Physics, Quantitative Finance, Statistics, or Computer Science
• Understanding of Asset pricing models (e.g., Black-Scholes, local volatility models, Greeks management)
• Knowledge of the MetaTrader platforms (MT4/MT5)
• Experience with AI (Claude.io, Copilot, Codex)

We offer

• 20 paid vacation days per year
• 10 paid sick leave days per year
• Public holidays as per the company’s approved Public holiday list
• Medical insurance
• Opportunity to work remotely
• Professional education budget
• Language learning budget
• Wellness budget (gym membership, sports gear and related expenses)

Simulmedia is a trailblazer in the world of data-driven advertising, known for its innovative approach to optimizing ad placements and campaign effectiveness through analytics and machine learning. Our mission is to revolutionize the advertising sector by enabling brands to reach their audiences more effectively.

We are seeking a motivated Middle Data Scientist to join our data science team. The ideal candidate has solid experience with supervised machine learning, strong analytical skills, and is comfortable working with real-world, large-scale data. In this role, you will contribute to building, improving, and maintaining machine learning models that support our advertising products and business decisions.

This position is based in Ukraine. The team primarily works remotely, with occasional in-person meetings in the Kyiv or Lviv office.

Key Responsibilities:

• Develop and maintain supervised machine learning models to support ad targeting, measurement, and campaign optimization.
• Perform data analysis, data validation, and feature engineering on large and complex datasets.
• Collaborate with product, engineering, and analytics teams to translate business needs into data science solutions.
• Evaluate model performance using appropriate metrics and iterate on model improvements.
• Support optimization and experimentation efforts related to advertising performance.
• Assist with deploying models to production in collaboration with engineering teams.
• Monitor model performance and help troubleshoot issues in production.
• Communicate results, insights, and limitations to both technical and non-technical stakeholders.
• Stay up to date with industry best practices in data science and machine learning.

Qualifications:

Education:

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

Experience:

• 2–4 years of experience in data science, machine learning, or a related analytical role.
• Hands-on experience applying machine learning to real business problems.

Technical Skills:

• Proficiency in Python.
• Solid experience with SQL and relational databases.
• Experience with machine learning libraries such as scikit-learn. Experience with TensorFlow or PyTorch is a plus.
• Good understanding of supervised learning techniques such as linear regression, logistic regression, and tree-based models including decision trees, random forests, and gradient boosting.
• Experience with data manipulation and analysis using pandas and NumPy.
• Familiarity with model evaluation techniques and performance metrics.
• Basic understanding of working with production systems or machine learning pipelines is a plus.

Other Skills:

• Strong analytical and problem-solving abilities.
• Ability to work independently while collaborating within a team.
• Clear communication skills and ability to explain technical concepts simply.
• Strong organizational skills and attention to detail.
• Comfortable communicating with U.S.-based teams.

About RiskSeal

RiskSeal is a B2B startup serving fintech clients globally. Our API-first credit risk management platform enables digital banks and consumer lenders to simplify onboarding through data-driven insights.

We are growing rapidly and value collaboration with passionate, creative professionals. Join us to innovate, contribute your expertise, and grow alongside our team.

Headquarters: Dallas, Texas, USA

European Office: Warsaw, Poland

Job Description

We are looking for a motivated Data Scientist to join our team in a fully remote role. The ideal candidate has experience in credit risk modelling, strong Python and SQL skills, as well as hands-on experience with scoring models. You will build and optimize advanced credit risk and anti-fraud models by leveraging machine learning, anomaly detection, and alternative data alongside traditional scoring. Working with client data, you will translate complex algorithmic outputs into clear, actionable insights for clients, partners, and internal teams.

What we're looking for:

• 2+ years of experience in credit risk modelling.
• Solid knowledge of classical methods (logistic regression, decision trees, scorecards) and ML (boosting, random forests, neural networks).
• Strong Python and SQL skills.
• Background in a bank, fintech, or data vendor environment.
• Experience with PD modelling, scoring, and portfolio performance metrics.
• Comfortable working end-to-end: from raw data to a validated, production-ready output.
• Able to present findings clearly to non-technical audiences, including clients and business partners.
• Upper-Intermediate English level or higher.

What you'll be doing:

• Build, test, validate, and implement predictive credit risk, scoring, and anti-fraud models.
• Analyze complex client data and alternative data streams to uncover hidden insights and deliver data-driven recommendations that guide strategic decisions.
• Continuously monitor and audit model performance (tracking drift, Gini, and PSI) to ensure long-term stability and accuracy.
• Design and experiment with new data attributes, leveraging digital footprint and transactional data to improve model predictability.
• Present analytical findings, model performance reports, and innovative ideas directly to clients, partners, and internal teams.
• Conceptualize new analytical products, scorecards, and advanced data solutions to expand RiskSeal's product offering.
• Partner closely with business, product, and tech teams to deploy models and seamlessly turn data insights into scalable, real-world value.

Nice to Have

• Experience working with alternative data sources.
• Knowledge of model governance and validation frameworks.
• Experience presenting analytical solutions directly to clients.
• Exposure to fintech products, digital lending, or embedded finance.

What We Offer

● A fully remote, full-time role in a growing product fintech company
● A fast-paced environment with minimal bureaucracy and quick decision-making
● The opportunity to work on credit risk models, scoring solutions, and real client data
● Direct collaboration with business, product, and tech teams
● The chance to contribute to analytical products that create real value for clients
● Competitive compensation and opportunities for professional growth