Contentstack - Senior/Principal Data Scientist - Generative AI

Job Overview :

We are seeking a highly accomplished and visionary Senior / Principal Data Scientist to lead our advanced analytics and AI initiatives. This pivotal role demands a deep understanding of cutting-edge machine learning techniques, particularly in Generative AI (GenAI) and Retrieval Augmented Generation (RAG).

You will be instrumental in solving complex business problems, from conceptualization and model development to deployment in production environments. Beyond technical mastery, this role requires strong leadership capabilities, including mentoring junior team members, driving projects, and effectively translating sophisticated technical insights for non-technical stakeholders, all while aligning with our overarching business strategy.

Responsibilities :

- Advanced Model Development : Apply an advanced understanding of supervised, unsupervised, and reinforcement learning to design and develop cutting-edge predictive and prescriptive models.

- Generative AI & RAG Expertise : Lead the development and implementation of solutions utilizing Generative AI (GenAI), Retrieval Augmented Generation (RAG), and advanced RAG techniques such as RAGA (Retrieval-Augmented Generation Agent), Query Expansion, and Knowledge Graph RAG (KGRAG).

- Statistical Analysis & Data Exploration : Apply strong knowledge of statistical modeling, hypothesis testing, and advanced data exploration techniques to derive actionable insights from complex datasets.

- Technical Leadership & Mentorship : Take a leadership role in data science and machine learning teams, including mentoring junior data scientists and fostering a collaborative, innovative, and high-performing environment.

- Production Deployment & MLOps : Drive the deployment of machine learning models into production environments, demonstrating strong familiarity with containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud platforms (AWS, GCP, Azure).

- Project & Stakeholder Management : Effectively manage data science projects, defining clear milestones, ensuring timelines are met, and communicating progress and results transparently to key stakeholders.

- Cross-functional Collaboration : Collaborate closely with software engineers and product managers to ensure seamless integration of data science solutions and alignment between technical initiatives and core business goals.

- Strategic Alignment : Develop a deep understanding of the company's overall business strategy and align data science initiatives to directly support these objectives, identifying high-impact opportunities.

- Communication & Storytelling : Possess the ability to translate complex ML/GenAI concepts and analytical findings into clear, actionable insights for non-technical stakeholders and executive leadership.

- Documentation & Best Practices : Create clear, thorough documentation for data science products, projects, experiments, and methodologies, ensuring maintainability and knowledge transfer.

- Research & Innovation : Continuously keep up with the latest research, tools, and trends in data science and machine learning, particularly in the rapidly evolving areas of GenAI and RAG, identifying opportunities for adoption.

- Complex Problem Solving : Identify, analyze, and address complex data and business challenges, often through the application of novel AI/ML approaches or hybrid solutions.

Required Technical Skills :

- Advanced understanding of supervised, unsupervised, and reinforcement learning algorithms and applications.

- Current, hands-on experience in Generative AI (GenAI), Retrieval Augmented Generation (RAG), and advanced RAG

techniques like RAGA, Query expansion, KGRAG, etc.

- Strong knowledge of statistical modeling, hypothesis testing, and advanced data exploration techniques.

- Proficiency in Python, including relevant data science libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).

- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).

- Experience with MongoDB for data storage and retrieval.

- Solid understanding of code optimization and clean coding practices.

- Strong theoretical foundation in probability, linear algebra, calculus, and statistical inference.

- Proven experience in deploying machine learning models in production environments.

- Familiarity with Docker, Kubernetes, and major cloud platforms (AWS, GCP, Azure) for MLOps.