 
						Data Scientist
5 days ago
Description :
Key Responsibilities :
- Design, develop, and deploy data science models - including classification, regression, and ideally survival analysis techniques.
- Build and productionize data products that deliver measurable business impact.
- Perform exploratory data analysis, feature engineering, model validation, and performance tuning.
- Apply statistical methods to uncover trends, anomalies, and actionable insights.
- Implement scalable solutions using Python (or R/Scala), SQL, and modern data science libraries.
- Stay up to date with advancements in NLP and Generative AI and evaluate their applicability to internal use cases.
- Communicate findings and recommendations clearly to both technical and non-technical stakeholders.
Qualifications : 
- Masters degree or certifications in Data Science, Machine Learning, or Applied Statistics is a strong plus.
Experience : 
- Demonstrated experience in end-to-end ML model development, from problem framing to deployment.
- Prior experience working with cross-functional business teams is highly desirable.
Must-Have Skills : 
- Business Problem Solving : Experience translating ambiguous business challenges into data science use cases.
- Model Development : Hands-on experience in building and validating machine learning models (classification, regression, survival analysis).
- Programming Proficiency : Strong skills in Python (Pandas, NumPy, Scikit-learn, Matplotlib/Seaborn), and SQL.
- Data Manipulation : Experience handling structured/unstructured datasets, performing EDA, and data cleaning.
- Communication : Ability to articulate complex technical concepts to non-technical audiences.
- Version Control & Collaboration : Familiarity with Git/GitHub and collaborative development practices.
- Deployment Mindset : Understanding of how to build data products that are usable, scalable, and maintainable.
Nice-to-Have Skills : 
- Exposure to Natural Language Processing (NLP) methods (e.g., tokenization, embeddings, sentiment analysis).
- Familiarity with Generative AI technologies (e.g., LLMs, transformers, prompt engineering).
- Experience with MLOps tools, pipeline orchestration (e.g., MLflow, Airflow), or cloud platforms (AWS, GCP, Azure).
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