Middle/Senior ML Engineer (Data Science)

We are looking for a Middle/Senior ML Engineer to turn loosely framed business problems into reliable, actionable models for a proprietary trading platform that connects traders with funding opportunities. You will work in a high-load, low-latency environment processing millions of events in real time, taking ownership from problem framing and feature engineering through model testing and business-ready results.

Required for this role

• Solid grounding in machine learning theory, with the ability to explain and justify modeling decisions beyond library calls
• Experience in building supervised (churn, customer decision) and unsupervised (clustering, identifying anomalies) risk models
• Experience building causal models (A/B testing, control groups, uplift modeling) and identifying why something happened
• Experience with survival analysis or time-to-event modeling
• Practical experience with XGBoost or other gradient boosting methods (GBM)
• Understanding of logistic regression and its practical application to business problems
• Hands-on experience with Python, NumPy, SciPy, Pandas, and Scikit-learn
• Experience working with PySpark, including MLlib, for data processing and machine learning
• Knowledge of hierarchical modeling methods and when to apply them
• Experience using MLflow or comparable experiment and model tracking tools
• Ability to independently frame a problem from rough context, engineer features, build and test models, and translate results into actions the business can use
• Strong communication and comfort with ambiguity, including asking questions and flagging unclear assumptions early
• Fluent spoken English (B2)

Nice to have

• Familiarity with Microsoft Fabric
• FinTech background.

Your responsibilities

• Take loosely framed business problems and develop appropriate ML solutions from scratch.
• Explore data, test hypotheses, and iterate on potential approaches in notebooks.
• Engineer features and build models using gradient boosting, logistic regression, survival analysis, or hierarchical methods, depending on the problem.
• Select rigorous, relevant evaluation metrics based on the business objective and modeling approach.
• Validate whether models hold up against the available data and intended use case.
• Translate experimental findings and model results into clear, actionable outcomes for the business.
• Challenge problem framing when assumptions do not hold, and communicate questions, risks, or blockers early.

What you get

Your time off

• 18 paid vacation days and 10 paid sick days annually
• 10 Ukrainian public holidays
• Maternity and paternity leaves
• Marriage and Parenthood Package
• Additional leave for major life events
• Sabbatical leave opportunities

Learning & growth

• Sombra University workshops and internal learning programs
• Tech Communities and knowledge sharing sessions
• Language courses and workshops
• Mentorship opportunities

Health & well-being

• Sports compensation or health insurance coverage
• Participation in races and marathons
• Corporate doctor (telemedicine)
• Well-being initiatives and workshops

And even more

• Company-provided technical equipment
• PE administration and tax support
• Internal referral program
• IT Club loyalty program
• Company events and volunteering initiatives

Before you apply

Our recruitment team will carefully review your profile, and if we see a good match with the role, we’ll reach out to you shortly.

If you don’t hear from us within 5 business days, it means we’ve decided to continue the process with other candidates for this position. Thanks for understanding.