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Senior Data Scientist (Hyderabad, Telangana) bei Providence India | Healthcare Technology Company

Providence India | Healthcare Technology Company · Hyderabad, Indien · Onsite

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Job Summary:

We are seeking a highly skilled and experienced Senior Data Scientist with deep expertise in statistical modeling, machine learning, and data-driven decision-making. The ideal candidate will lead complex analytical projects, design robust predictive models, and collaborate cross-functionally to drive strategic insights and innovation.


Key Responsibilities:

  • Lead end-to-end data science projects from problem definition to model deployment.
  • Apply advanced statistical techniques (e.g., hypothesis testing, Bayesian inference, time series analysis) to solve business problems.
  • Design, build, and validate machine learning models for classification, regression, clustering, and recommendation systems.
  • Interpret model results and communicate findings to stakeholders with clarity and impact.
  • Collaborate with data engineers to ensure scalable data pipelines and model integration.
  • Mentor junior data scientists and contribute to best practices in model development and evaluation.
  • Stay current with the latest research and trends in ML/AI and apply them to real-world problems.

Required Qualifications:

  • Master’s or Ph.D. in Statistics, Mathematics, Computer Science, or a related field.
  • 3-5+ years of experience in data science, with a strong portfolio of statistical and ML projects.
  • Proficiency in Python or R, and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
  • Good Understanding in NLP, NER Techniques, RAG pipelines, Multi-Agent RAG pipelines,  Hybrid search and context similarity techniques.
  • Strong foundation in statistical theory and its practical applications.
  • Experience with cloud platforms (Snowpark, Azure ML) and MLOps tools is a plus.
  • Excellent communication and storytelling skills with data.

Preferred Skills:

  • Experience in causal inference, A/B testing, and experimental design.
  • Familiarity with deep learning architectures and NLP techniques.
  • Exposure to big data tools like Spark, Snowflake, or Databricks.
  • Domain expertise in [e.g. healthcare] is a plus.

 

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