Responsibilities:
Role: Data Scientist
- Type: Full-Time
- Level: Mid to Senior (3-5 Years of Experience)

About the Role
- Key Responsibilities- Analyze large, complex datasets to uncover patterns, trends, and business insights
- Build, train, and evaluate machine learning and statistical models
- Design and implement end-to-end ML pipelines from data ingestion to model serving
- Collaborate with engineering teams to deploy models into production
- Define KPIs and build dashboards to track model performance and data quality
- Conduct A/B testing and experimentation to validate hypotheses
- Work with stakeholders to translate business problems into data science solutions
- Document methodologies and present findings to technical and non-technical audiences

What We're Looking For
- A curious, rigorous thinker who is equally comfortable in a Jupyter notebook and a production codebase. You can independently drive analysis from raw data to deployed model, and communicate results clearly to both technical and business stakeholders.

Profile required:
Required Skills:




- 3-5 years of experience as a Data Scientist or in a similar analytical/ML role
- Robust proficiency in Python (pandas, NumPy, scikit-learn, matplotlib, seaborn)
- Experience with supervised and unsupervised learning algorithms
- Solid understanding of statistics: hypothesis testing, regression, distributions, Bayesian methods
- Experience with SQL and working with relational databases
- Familiarity with ML frameworks: TensorFlow, PyTorch, or XGBoost / LightGBM
- Experience with data visualization tools (Matplotlib, Plotly, Power BI, or Tableau)
- Understanding of model evaluation, cross-validation, and hyperparameter tuning
- Nice to Have- Experience with MLflow, DVC, or similar experiment tracking tools
- Exposure to LLMs, fine-tuning, or GenAI use cases
- Familiarity with cloud ML platforms: Azure ML, AWS SageMaker, or Vertex AI
- Knowledge of feature stores and model registries
- Experience with PySpar

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