About the Role :

We are looking for an Experienced Data Scientist to join our team and deliver high-impact, data-driven solutions directly with our clients. This is a client-facing role you will engage with business stakeholders to understand their goals, translate them into analytical and AI-driven solutions, and own delivery end-to-end.

You will work alongside a talented team of machine learning engineers and data scientists, bringing both technical depth and strong communication to every engagement.

What You'll Do :

- Partner with clients and business stakeholders to understand desired outcomes and define the right data science approach.

- Design, build, and deploy machine learning and AI solutions from raw data to production.

- Develop models from scratch, including data preparation, feature engineering, training, evaluation, and deployment.

- Design and run experiments, research emerging algorithms, and continuously improve model performance.

- Build and implement agentic AI and Retrieval-Augmented Generation (RAG) pipelines to power intelligent, context-aware applications.

- Analyze model performance, feature importance, and business impact and communicate findings clearly to both technical and non-technical audiences.

- Contribute to ML pipelines and deployment infrastructure.

- Experiment with new technologies and approaches with a genuine learner's mindset.

Required Qualifications :

Experience & Expertise :

- 3-4 years of experience in machine learning/ statistical modelling.

- Proven track record of applying theoretical models in applied, real-world environments.

- Hands-on experience building GenAI solutions using Large Language Models (LLMs).

- Familiarity with fine-tuning LLMs (prior fine-tuning experience would be great to have but not required).

- Deep understanding of ML algorithms including regression, classification, clustering, boosting, and neural networks.

Agentic AI & Modern AI Systems :

- Understanding of agentic AI architectures and workflows.

- Experience with Retrieval-Augmented Generation (RAG) including document chunking strategies, embedding models, and retrieval pipelines.

- Familiarity with vector databases (e.g. Pinecone, Weaviate, Chroma) and their role in semantic search and AI applications.

- Awareness of prompt engineering, tool use, and orchestration frameworks (e.g. LangChain, LlamaIndex).

Technical Skills :

- Advanced proficiency in SQL and Python.

- Strong command of Python libraries : NumPy, Pandas, Scikit-learn, Matplotlib.

- Experience with key metrics analysis and experimentation : A/B testing, stratification, propensity score matching, causal trees.

- Familiarity with ML pipelines and model deployment.

Communication & Client Engagement :

- Strong ability to present analytical findings and model logic clearly to both technical and non-technical stakeholders.

- Experience working directly with clients or business teams to scope problems and shape solutions.

- Comfortable navigating ambiguity and translating business questions into data science problems.

Preferred Qualifications :

- Undergraduate studies in a quantitative field : Computer Science/ Computer engineering.

- Experience with cloud-based ML platforms (GCP is preferred but not required).

- Exposure to MLOps practices and tools.