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
Practical experience with XGBoost or other gradient boosting methods (GBM)
Understanding of logistic regression and its practical application to business problems
Experience building causal models (A/B testing, control groups, uplift modeling) and identifying why something happened
Hands-on experience with Python, NumPy, SciPy, Pandas, and Scikit-learn
Experience working with PySpark, including MLlib, for data processing and machine learning
Experience with survival analysis or time-to-event modeling
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.