Mid-to-Senior Machine Learning Engineer / Data Scientist

About the Role We’re 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.) and 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, 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 (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.
• Collaboration & Productionization: Work across the ML lifecycle often in partnership with client stakeholders and internal delivery teams, turning modeling work into production-ready systems and ensuring monitoring and governance post-deployment. What We're Looking For
• Strong foundation in data science and applied machine learning; comfortable working with real-world data; ability to translate business questions into actionable ML solutions.
• Experience across the ML lifecycle: problem framing, data exploration, feature engineering, model development, evaluation, deployment, and monitoring.
• Proficiency with SQL and Python; experience with relational databases and data warehouses; ability to build robust feature pipelines and implement proper modeling best practices.
• Hands-on experience with a range of models for tabular data and time series, including logistic/linear models, tree-based methods, gradient boosting frameworks (XGBoost/LightGBM/CatBoost), and neural networks for structured data.
• Strong knowledge of time series modeling, clustering, hypothesis testing, experiment design, and cross-validation.
• Ability to collaborate with client stakeholders and internal delivery teams; comfortable turning modeling work into production-ready systems. Nice to Have
• Experience with deploying ML models into production and monitoring their performance in a client-facing environment.
• Familiarity with PyTorch-based deep learning workflows and tooling. Compensation & Benefits
• Salary details and benefits are not disclosed in the posting. This is a full-time remote opportunity.