Data Scientist

Data Scientist

Experience:

Job Summary

Data Scientist

Key Responsibilities

• Develop and deploy

  machine learning and predictive analytics models

  to solve business problems.
• Work on

  predictive and prescriptive analytics

  use cases.
• Perform data preparation, exploration, feature engineering, and model development.
• Build scalable data-processing solutions using

  Databricks and Apache Spark

  .
• Develop and optimize machine learning pipelines.
• Perform

  feature engineering and feature selection

  to improve model performance.
• Evaluate models using appropriate statistical and ML metrics.
• Use

  MLflow

  for experiment tracking, model management, and deployment workflows.
• Implement

  MLOps practices

  for model lifecycle management and productionization.
• Monitor model performance and identify model/data drift where applicable.
• Work with structured and unstructured datasets to identify meaningful patterns and insights.
• Create data visualizations and communicate analytical findings to technical and business stakeholders.
• Follow data governance, data quality, security, and compliance standards.
• Collaborate with Data Engineers, Business Analysts, ML Engineers, and other stakeholders.
• Document models, experiments, methodologies, and results.

Required Skills

• 3–5 years of experience

  in Data Science / Machine Learning.
• Strong understanding of

  Machine Learning and Statistical Modeling

  .
• Hands-on experience with

  Predictive Analytics

  .
• Knowledge of

  Prescriptive Analytics

  and optimization-based decision-making.
• Strong experience with

  Python

  and relevant data science libraries.
• Hands-on experience with

  Databricks

  .
• Good knowledge of

  Apache Spark / PySpark

  .
• Strong experience in

  Feature Engineering

  .
• Experience with

  MLflow

  and ML lifecycle management.
• Understanding of

  MLOps

  concepts and practices.
• Experience with

  Model Evaluation and Validation

  techniques.
• Good understanding of data governance and data quality.
• Experience creating effective

  data visualizations

  and communicating insights.

Good to Have

• Experience with cloud platforms such as

  AWS, Azure, or GCP

  .
• Experience with CI/CD for ML workflows.
• Knowledge of Docker/Kubernetes.
• Experience with model deployment and APIs.
• Knowledge of model monitoring and drift detection.
• Experience with NLP, recommendation systems, forecasting, or optimization.
• Familiarity with Delta Lake and Databricks ML capabilities.