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Job Description: Manager Data Scientist / AI

Role Overview:

We are seeking an accomplished and Manager Data Scientist with minimum 8 Years of experience in AI/ML, statistics, deep learning, neural networks, and ML engineering skills to lead our AI team and drive the strategic direction of AI initiatives. The candidate will be responsible for designing, developing, and deploying cutting-edge machine learning solutions to solve real world analytics problems in various domains such as, natural language processing, predictive analytics, recommender systems, etc. The ideal candidate should have a proven track record in AI leadership, a deep understanding of AI technologies, and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Minimum 8 Years of experience in Data Science and Machine learning. Excellent leadership skills with at least 2-3 years of people management OR technical architecture experience.

Responsibilities:

Your technical responsibilities:

• Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing AI solutions.
• Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance.
• Lead a team of machine learning engineers and data scientists to develop and deploy machine learning models using cloud services including AWS, Azure etc.
• Collaborate with stakeholders to identify business opportunities, define AI project goals, and prioritize initiatives based on strategic objectives.
• Stay updated with the latest advancements in Data Science / AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models, Anthropic, Cohere, Meta, Mistral AI and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases.
• Ensure compliance with data privacy, security, and ethical considerations in AI applications.
• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.

Requirements:

• Bachelor's or Master's degree in Statistics, Maths, Computer Science, Engineering, or a related field. A Ph.D. is a plus.
• Proven experience in leading and managing Data Science / AI projects and teams in the pharmaceutical industry.
• Experience of solving problems in patient claims analytics, Next Best Action, Personalisation, recommendation engine etc.
• Demonstrated knowledge of pharma real world datasets including claims, electronic medical records, sales etc.
• Proven experience in building and deploying machine learning models using open source and cloud services (AWS, Azure etc.)
• In-depth knowledge of machine learning, deep learning, and generative AI techniques.
• Proficiency in programming languages such as Python, R and frameworks like TensorFlow or PyTorch.
• Expertise in data engineering, including data curation, cleaning, and pre-processing.
• Experience in DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.
• Familiarity with tools such as Docker, Kubernetes, and Git for building and managing AI pipelines.
• Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models.
• Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions.
• Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels.
• Track record of driving innovation and staying updated with the latest AI research and advancements.

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