Data Science Lead

Job Title: Lead Data Scientist

Location: Indore

Job Type: Full-time

Job Description: As the Lead Data Scientist for Machine Learning, you will lead a team of data scientists and engineers to design, develop, and deploy state-of-the-art machine learning models and algorithms. Your work will directly contribute to advancing our core products and optimizing data-driven decision-making processes across the organization. You will collaborate with cross-functional teams to identify key business challenges, formulate data science strategies, and drive innovation in areas such as predictive analytics, automation, and personalization.

Key Responsibilities:

• Lead a team of data scientists and machine learning engineers, providing mentorship, guidance, and hands-on support in model development, research, and deployment.
• Design and develop advanced machine learning models for a variety of business applications, including predictive analytics, recommendation systems, natural language processing, and computer vision.
• Collaborate with product managers, business leaders, and software engineers to translate business requirements into actionable data science projects.
• Drive the adoption of best practices in machine learning, ensuring that models are robust, scalable, interpretable, and aligned with business goals.
• Conduct exploratory data analysis (EDA), model validation, and performance evaluation to ensure high-quality, reliable models.
• Stay up to date with the latest trends, tools, and techniques in the field of machine learning and AI, and share knowledge with the broader team.
• Advocate for data-driven decision-making within the organization and communicate complex technical findings to non-technical stakeholders.
• Lead and contribute to the research and development of innovative machine learning algorithms and techniques to solve unique business problems.
• Build and maintain effective pipelines for model training, testing, and deployment.
• Take ownership of the entire machine learning lifecycle, from ideation to deployment and optimization.

Qualifications:

• Bachelors or Higher in Computer Science, Statistics, Mathematics, or a related field from top tier college.
• 5+ years of experience in data science or machine learning roles, with at least 2+ years of leadership experience.
• Expertise in machine learning algorithms, statistical methods, and data analysis techniques (e.g., regression, classification, clustering, time series, deep learning, etc.).
• Proficiency in programming languages such as Python, R, or Scala, with strong experience using machine learning frameworks like TensorFlow, PyTorch, Scikit-learn, or similar.
• Strong experience with data processing and manipulation using tools like Pandas, NumPy, and SQL.
• Solid understanding of cloud platforms (AWS, GCP, Azure) and containerization tools (e.g., Docker, Kubernetes) for model deployment and scaling.
• Experience in deploying machine learning models into production environments and monitoring model performance.
• Proven track record of working with large datasets, designing scalable data pipelines, and leveraging big data technologies (e.g., Hadoop, Spark).
• Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
• A strong problem-solving mindset, with an ability to tackle challenging, unstructured problems and create innovative solutions.
• Experience in agile methodologies and working in cross-functional teams.

Preferred Qualifications:

• Experience with reinforcement learning, deep learning, and natural language processing.
• Knowledge of advanced model interpretability tools and techniques.
• Familiarity with software engineering best practices such as version control (Git), testing, and CI/CD pipelines.
• Experience in the deployment and monitoring of machine learning models at scale.
• Experience and Familiarity with machine learning operations(MLOps)

Benefits:

• Competitive salary
• Generous paid time off and holidays
• Professional development opportunities and continuous learning support
• Collaborative, inclusive company culture