I&F Decision Sci Practitioner Manager
Designation: I&F Decision Sci Practitioner Manager
Qualifications:Any Graduation
Years of Experience:13 to 18 years
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What would you do? An Artificial Intelligence Data Scientist Manager leads the design and deployment of advanced AI methodologies such as NLP, Machine Learning, and Deep Learning to solve complex business challenges, overseeing the entire project lifecycle from data acquisition to post-production monitoring
What are we looking for? This role involves setting strategic direction for data science initiatives, collaborating with cross-functional teams to implement scalable AI models, ensuring responsible AI practices, mentoring team members, and communicating insights effectively to both technical and non-technical stakeholders while staying current with emerging AI developments.
Roles and Responsibilities: Lead the design, development, and deployment of advanced AI and data science methodologies, including NLP, Machine Learning, and Deep Learning, to deliver scalable solutions for complex business challenges. Oversee the end-to-end lifecycle of large-scale data science and AI projects, from data acquisition and feature engineering to model development, deployment, and post-production monitoring, ensuring measurable business value. Provide strategic direction to the data science roadmap, identifying high-impact opportunities for innovation and guiding the team toward delivering actionable, production-ready insights. Represent the data science team in executive forums, steering committees, and client engagements, advocating for data-driven decision-making and championing enterprise-wide adoption of applied AI solutions. Collaborate with cross-functional teams—including product, engineering, and business stakeholders—to conceptualize, prototype, and implement scalable AI models across areas such as Natural Language Processing, Predictive Analytics, Recommender Systems, and Large Language Models (LLMs). Design robust experimentation frameworks, validate algorithms, and ensure compliance with responsible AI practices around fairness, transparency, and interpretability. Stay abreast of emerging developments in AI/ML, data engineering, and MLOps, integrating state-of-the-art techniques into business applications and contributing thought leadership through internal knowledge sharing. Translate complex analytical outcomes into clear, impactful narratives for both technical and non-technical stakeholders, providing evidence-based insights to inform strategic decisions. Mentor and guide a team of data scientists and analysts, fostering a culture of technical excellence, continuous learning, and collaboration.
Bengaluru
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