Machine Learning Engineer / Data Scientist

Job Description

As a skilled Machine Learning Engineer / Data Scientist, your role will involve designing, building, and deploying end-to-end ML solutions that drive measurable business impact. You will be responsible for the full ML lifecyclefrom 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.Qualifications Required:- Strong Python skills (pandas, numpy, scikit-learn).- Strong SQL skills (joins, window functions, aggregations).- Solid foundation in Statistics & Experimentation.- Hands-on experience in Classification & Regression, Time Series Forecasting, Clustering & Segmentation, and 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.Additional Details: The company prefers candidates with experience in Databricks (Spark, Delta Lake, MLflow), MLOps practices, orchestration tools (Airflow / Prefect / Dagster), modern data platforms (Snowflake / BigQuery / Redshift), cloud platforms (AWS / GCP / Azure / IBM), containerization (Docker), responsible AI & governance practices, and client-facing / consulting experience.Certifications (Strong Plus): Cloud certifications (AWS / GCP / Azure / IBM Data/AI tracks) and Databricks certifications (Data Scientist / Data Engineer) are considered advantageous for this role. As a skilled Machine Learning Engineer / Data Scientist, your role will involve designing, building, and deploying end-to-end ML solutions that drive measurable business impact. You will be responsible for the full ML lifecyclefrom 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.Qualifications Required:- Strong Python skills (pandas, numpy, scikit-learn).- Strong SQL skills (joins, window functions, aggregations).- Solid foundation in Statistics & Experimentation.- Hands-on experience in Classification & Regression, Time Series Forecasting, Clustering & Segmentation, and 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.Additional Details: The company prefers candidates with experience in Databricks (Spark, Delta Lake, MLflow), MLOps practices, orchestration tools (Airflow / Prefect / Dagster), modern data platforms (Snowflake / BigQuery / Redshift), cloud platforms (AWS / GCP / Azure / IBM), containerization (Docker), responsible AI & governance practices, and client-facing / consulting experience.Certifications (Strong Plus): Cloud certifications (AWS / GCP / Azure / IBM Data/AI tracks) and Databricks certifications (Data Scientist / Data Engineer) are considered advantageous for this role.

Keyskills :
PythonSQLStatisticsMachine LearningDeep LearningCommunicationStakeholder ManagementContainerizationPyTorchTensorFlowModel EvaluationCrossValidationData HandlingDatabricksMLOpsOrchestration ToolsCloud PlatformsResponsible AIClientfacing E