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)