Lead Data Scientist

Job Responsibilities Analyze complex data sets to identify trends, patterns, and insights that can drive business decisions.

Design and develop predictive models, algorithms, machine learning, and artificial intelligence techniques to improve the accuracy and efficiency of analytics solutions

Collaborate with cross-functional teams to ensure the data used is accurate, relevant, and up-to-date for analytics purposes.

Contribute to hands-on development of data & analytics solutions.

Deliver products and solutions in a timely, proactive, and entrepreneurial manner.

Accelerate solution delivery using re-usable frameworks, prototypes and hackathons.

Follow MLOps principles to ensure scalability, repeatability, and automation in the end-to end machine learning lifecycle.

Developing and maintaining detailed technical documentation.

Stay up-to-date on industry trends and new technologies in data science and analytics, and apply this knowledge to improve the firm's analytics capabilities

Education, Technical Skills & Other Critical Requirement

0-5 years of relevant experience in AI / analytics product & solution delivery

Bachelor’s / Master’s degree in an information technology / computer science / Statistics / Economics or equivalent fields experience.

Strong understanding of Machine Learning and Deep Learning concepts, with a focus on Natural Language Processing.

Proficiency in Python programming language, with experience using libraries like PyTorch for deep learning tasks.

Familiarity with Elastic stack (Elasticsearch, Logstash, Kibana) for data management and analysis.

Experience in optimizing algorithms and time series forecasts.

Knowledge of prompt engineering techniques to improve model performance.

Ability to prototype applications using Streamlit or similar tools.

Experience working with large and complex internal, external, structured and unstructured datasets.

• Model development and deployment in cloud;

Familiarity with Github, CI / CD process, Docker, Containerization & Kubernetes.

Strong conceptual and creative problem-solving skills

engage in meaningful manner with variety of audience : business stakeholders, technology partners & practitioners, executive and senior management.

Industry knowledge about emerging AI trends, AI tools and technologies

Preferred :

Familiarity of new age AI and ML techniques such as GenAI, foundational models, large language models (LLMs) and applications

Certifications in AI space such as ML Ops, AI Ops, Generative AI, Ethical AI, AI deployment in cloud

Familiarity with agile methodologies & tools

Prior experience in P&C Insurance Analytics

Prior experience in Analytics consulting and services