Machine Learning Engineer / Data Scientist

Job Title: Machine Learning Engineer / Data Scientist

Work Mode-Remote || Contract Role

Experience: 4–8 Years (Mid–Senior Level)

Role Summary

We are seeking a skilled Machine Learning Engineer / Data Scientist to design, build, and deploy end-to-end ML solutions that drive measurable business impact. The role spans the full ML lifecycle—from problem framing and data exploration to modeling, deployment, monitoring, and stakeholder communication.

Key Responsibilities

Translate business problems into ML solutions (classification, regression, time series, clustering, anomaly detection, recommendations). Perform data extraction and analysis using SQL and Python . Build robust feature engineering pipelines and prevent data leakage. Develop and tune ML models (XGBoost, LightGBM, CatBoost, neural networks). Apply statistical methods (hypothesis testing, experiment design, confidence intervals). Develop time series forecasting models with proper backtesting. Build deep learning models using PyTorch or TensorFlow/Keras . Evaluate models using appropriate metrics (AUC, F1, RMSE, MAE, MAPE, business KPIs). Support production deployment (batch/API) and implement monitoring & retraining strategies. Communicate insights and recommendations to technical and non-technical stakeholders.

Required Skills

Strong Python (pandas, numpy, scikit-learn) Strong SQL (joins, window functions, aggregations) Solid foundation in Statistics & Experimentation Hands-on experience in: Classification & Regression Time Series Forecasting Clustering & Segmentation Deep Learning (PyTorch / TensorFlow) Experience with model evaluation, cross-validation, calibration, and explainability (e.g., SHAP). Ability to handle messy data and ambiguous business problems. Strong communication and stakeholder management skills.

Preferred Skills

Experience with Databricks (Spark, Delta Lake, MLflow) MLOps practices (model versioning, monitoring, retraining pipelines) Orchestration tools: Airflow / Prefect / Dagster Modern data platforms: Snowflake / BigQuery / Redshift Cloud platforms: AWS / GCP / Azure / IBM Containerization (Docker) Responsible AI & governance practices Client-facing / consulting experience

Nice to Have

Causal inference & uplift modeling Agentic workflow development (tool use, planning, memory, guardrails) Experience with AI-assisted development tools and code agents

Certifications (Strong Plus)

Cloud certifications (AWS / GCP / Azure / IBM – Data/AI tracks) Databricks certifications (Data Scientist / Data Engineer)