Job Description

Key Responsibilities:

• Collect, clean, and preprocess large and complex datasets from multiple sources.

• Design, develop, and implement machine learning (ML) and statistical models to address business challenges.

• Analyze data trends and patterns to generate actionable insights for business teams.

• Work closely with data engineers , analysts , and stakeholders to understand business objectives and deliver data-driven solutions.

• Develop and deploy models into production using tools such as Azure ML , AWS Sagemaker , or Databricks .

• Evaluate model performance using relevant metrics and continuously improve accuracy and scalability.

• Create clear and compelling data visualizations and presentations to communicate insights.

• Collaborate in Agile teams and participate in sprint planning, stand-ups, and reviews.

• Maintain detailed documentation of experiments, methodologies, and results.

• Stay updated on emerging technologies and trends in data science, AI, and ML.

Required Skills & Qualifications:

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

• Strong programming skills in Python or R , with experience using libraries such as pandas , NumPy , scikit-learn , TensorFlow , or PyTorch .

• Solid understanding of machine learning algorithms , statistical analysis , and predictive modeling techniques .

• Proficiency in SQL and working with relational databases (e.g., SQL Server, PostgreSQL).

• Experience with data visualization tools such as Power BI , Tableau , or Matplotlib/Seaborn .

• Hands-on experience with cloud platforms (Azure, AWS, or GCP) for data and ML workflows.

• Familiarity with data engineering tools (e.g., Azure Data Factory, Databricks, or Spark) for building data pipelines.

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

• Ability to work collaboratively with cross-functional teams in an Agile environment.

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