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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