Machine Learning Engineer​/Data Scientist

Position: 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, Light

GBM, Cat Boost, 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  Tensor Flow/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 / Tensor Flow)

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 / Big Query / 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)