Key Responsibilities :

- Collect, clean, process, and analyze large and complex datasets.

- Develop and deploy machine learning and statistical models to solve business problems.

- Perform exploratory data analysis and identify trends, patterns, and opportunities.

- Build predictive, classification, recommendation, and forecasting models as required.

- Define and track key business and product metrics.

- Conduct A/B testing and statistical analysis to support business decisions.

- Create data visualizations and communicate insights to technical and non-technical stakeholders.

- Collaborate with Data Engineers and Software Engineers to productionize models and analytical solutions.

- Monitor model performance and continuously improve model accuracy and reliability.

- Translate business requirements into analytical approaches and measurable outcomes.

- Stay current with developments in machine learning, AI, statistics, and data science.

Required Qualifications :

- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.

- 3 - 5 years of experience in data science, machine learning, analytics, or a related role.

- Strong programming skills in Python or R.

- Strong knowledge of SQL and relational databases.

- Hands-on experience with machine learning algorithms and statistical techniques.

- Experience with libraries/frameworks such as Pandas, NumPy, Scikit-learn, and preferably TensorFlow or PyTorch.

- Strong understanding of statistics, probability, hypothesis testing, and experimental design.

- Experience with data visualization tools such as Power BI, Tableau, or Python visualization libraries.

- Strong problem-solving, analytical, and communication skills.

Preferred Qualifications :

- Experience working with cloud platforms such as AWS, Azure, or GCP.

- Knowledge of MLOps and model deployment practices.

- Experience with Spark, Databricks, or other big-data technologies.

- Exposure to NLP, deep learning, recommendation systems, or generative AI.

- Experience deploying machine learning models through APIs or production pipelines.

- Familiarity with Git, Docker, and CI/CD practices.