Chief Machine Learning Strategist

3 days ago

Alwar, Rajasthan, India beBeeDataScientist Full time ₹ 2,40,00,000 - ₹ 4,00,00,000
Lead Data Scientist

Our team is driving cutting-edge innovation in machine learning at a leading real estate advertising platform. We're embracing a comprehensive approach to ML, with opportunities to work across all aspects of the field: predictive models, time-series forecasting, recommender systems, computer vision, and Gen AI-powered agents.

We're seeking a seasoned Lead Data Scientist to spearhead applied research and development of advanced AI/ML solutions. Your expertise will shape our AI strategy and guide data-driven decision-making, influencing product innovation and operational excellence across the organization.

As a leader, you'll partner with engineering, design, and product teams to operationalize ML models, ensuring seamless integration and measurable business impact. You'll also provide ML thought leadership and lead high-performing teams, enforcing best practices from experimentation through production deployment.

  • Architecting and deploying end-to-end ML pipelines leveraging state-of-the-art deep learning, NLP, time-series modelling, and image analysis techniques
  • Designing scalable, maintainable AI/ML platforms that deliver robustness, reliability, and alignment with enterprise objectives
  • Partnering with cross-functional teams to operationalize ML models and drive business impact
  • Providing ML thought leadership and leading high-performing teams to enforce best practices
  • Shaping AI strategy and guiding data-driven decision-making

Requirements:

  • Graduate/Post-graduate from Tier-I institutes (IITs, IIITs, NITs) with 5–8 years of applied experience in AI/ML
  • Proven leadership (2+ years) in leading teams and driving ML projects, with a track record of building strong Data Science practices and fostering high-performance culture
  • Advanced ML Expertise: Proficiency in ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn) and Python, with hands-on experience in deep learning architectures (Transformers, CNNs, RNNs, LSTMs), statistical modelling, and scalable deployment using distributed systems
  • Generative AI & Applied Innovation: Practical experience with LLM fine-tuning, RAG pipelines, embeddings, and prompt engineering; familiarity with GenAI ecosystems and integration into production applications
  • End-to-End Impact & Collaboration: Strong background in data wrangling, feature engineering, and MLOps, combined with proven ability to collaborate across engineering, product, and business teams and communicate complex concepts effectively

A passion for solving complex problems with the courage to take research risks and drive innovation is essential for success in this role.

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