Lead Data Scientist

Job description

A seasoned Data Scientist with 6-7 years of professional experience. This role offers the opportunity to leverage expertise in statistical analysis and AI/ML to develop impactful solutions that align with our enterprise strategy. Data Scientist will be deeply involved in the entire project lifecycle—from data preparation and exploratory analysis to model deployment—while collaborating with multidisciplinary teams to deliver scalable, measurable results.

Key Responsibilities

• Develop and implement high-impact AI/ML use cases that support our organizational objectives.
• Communicate findings, insights, and methodologies clearly to non-technical stakeholders.
• Design, build, and optimize predictive models, classifiers, and regression algorithms using classical AI/ML techniques such as SVMs, Decision Trees, Random Forests, k-NN, Naive Bayes, and ensemble methods.
• Validate models with appropriate statistical and machine learning evaluation techniques.
• Apply strong statistical foundations, including distributions, hypothesis testing, regression analysis, and probability theory.
• Conduct thorough exploratory data analysis to uncover key trends, patterns, and anomalies.
• Ensure data quality and reliability through rigorous analytical practices.
• Support the ML lifecycle, including model design, infrastructure, production setup, monitoring, and release management, with basic familiarity in MLOps.

Requirements

- 6-7 years of hands-on experience in Data Science.

• Proven proficiency in statistical data analysis, machine learning, and natural language processing, with a strong understanding of practical constraints.(Must Have)
• Advanced skills in Python programming and SQL, utilizing relevant libraries for effective data analysis. (Must Have)
• Demonstrated experience in AI/ML solution development, including supervised and unsupervised learning algorithms, model evaluation, and feature engineering. (Must Have)
• Basic familiarity with MLOps and feature engineering methods for model workflows.(Basic Knowledge)
• Competency in software development methodologies and versioning tools. (Must Have)
• Experience with front-end visualization tools such as Streamlit or lightweight UI layers (Good to Have).
• Exposure to GenAI, including LLM integration, prompt engineering, model packaging, and lifecycle management (Preferred).
• Familiarity with agentic AI frameworks like LangChain and LangGraph, and agent-based patterns (Good to have