Machine Learning Engineer / Data Scientist

Job title: Machine Learning Engineer / Data Scientist

About the Role

We are hiring a mid-to-senior Machine Learning Engineer / Data Scientist to build and deploy machine learning solutions that drive measurable business impact. You’ll work across the ML lifecycle—from problem framing and data exploration to model development, evaluation, deployment, and monitoring—often in partnership with client stakeholders and internal delivery teams. You should be strong in core data science and applied machine learning, comfortable working with real-world data, and capable of turning modeling work into production-ready systems.

What You'll Do

• Problem Framing & Stakeholder Partnership: Translate business questions into ML problem statements (classification, regression, time series forecasting, clustering, anomaly detection, recommendation, etc.). Collaborate with stakeholders to define success metrics, evaluation plans, and practical constraints (latency, interpretability, cost, data availability).
• Data Analysis & Feature Engineering: Use SQL and Python to extract, join, and analyze data from relational databases and data warehouses. Perform data profiling, missingness analysis, leakage checks, and exploratory analysis to guide modeling choices. Build robust feature pipelines (aggregation, encoding, scaling, embeddings where appropriate) and document assumptions.
• Model Development (Core ML): Train and tune supervised learning models for tabular data (e.g., logistic/linear models, tree-based methods, gradient boosting such as XGBoost/LightGBM/CatBoost, and neural nets for structured data). Apply strong tabular modeling practices: handling missing data, categorical encoding, leakage prevention, class imbalance strategies, calibration, and robust cross-validation. Build time series models (statistical and ML/DL approaches) and validate with proper backtesting. Apply clustering and segmentation techniques (k-means, hierarchical, DBSCAN, Gaussian mixtures) and evaluate stability and usefulness. Apply statistics in practice (hypothesis testing, confidence intervals, sampling, experiment design) to support inference and decision-making.
• Deep Learning: Build and train deep learning models using PyTorch.

What We're Looking For

• Strong foundation in core data science and applied machine learning, with experience handling real-world data and turning models into production-ready systems.
• Proficiency with SQL and Python for data extraction, analysis, and feature engineering.
• Experience training and evaluating supervised models for tabular data, including tree-based methods and gradient boosting (XGBoost/LightGBM/CatBoost) and time series modeling.
• Familiarity with model evaluation, cross-validation, leakage prevention, handling missing data, encoding of categorical variables, and calibration.
• Ability to work with stakeholders, translate business needs into ML problems, define success metrics, and design robust experimentation plans (A/B tests, backtesting).
• Experience with clustering/segmentation techniques and basic statistical inference (hypothesis testing, confidence intervals).
• Exposure to deep learning tools (e.g., PyTorch) is a plus.

Industry

Software, Finance, Healthcare, Entertainment, or similar