Senior Data Scientist

Solenis; Hyderabad; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Solenis GSS India is seeking a Senior / Principal Data & AI Scientist to lead and execute end-to-end AI initiatives across commercial operations, supply chain, and pricing strategy. This role is hands-on IC with technical leadership, focusing on ML, Generative AI, and enterprise-grade deployment.

The ideal candidate has 10–18+ years of experience, strong ownership, and ability to deliver impactful ROI while mentoring teams in a hybrid Hyderabad-based setup.

Qualifications

• 10+ years of AI/ML delivery with end-to-end ownership.
• Strong foundation in classical ML, predictive modeling, and enterprise-scale workflows.
• Experience deploying ML solutions with MLOps and ROI alignment.

Responsibilities

• Architect and deploy high-performance ML models for B2B challenges.
• Develop Generative AI workflows and multi-agent systems.
• Collaborate with product and executives to translate problems into scalable roadmaps.
• Lead technical reviews and mentor junior data scientists and engineers.

Skills

Python

R

PySpark

Pandas

NumPy

Scikit-learn

SQL

Supervised Learning

Unsupervised Learning

Time-Series

NLP

TensorFlow

PyTorch

LangChain

LangGraph

HuggingFace

BERT

Embeddings

FAISS

Pinecone

pgvector

Azure ML

Databricks

AWS

GCP

Docker

Kubernetes

MLflow

Education

Bachelor's in CS/DS/Engineering

Master's degree preferred (IIT/BITS/ISB)

Tools

Azure ML

Databricks

AWS

GCP

Job description

Role: Senior / Principal Data & AI Scientist

Experience: 10 Years - 18+ Years

Company: Solenis GSS India

Location: Hyderabad, India (Hybrid)

Employment Type: Full-Time

About the Role

Solenis GSS India is looking for an elite Senior / Principal Data & AI Scientist to drive innovation within our core AI & Data Science team. This is a high-impact, hands-on Individual Contributor (IC) and technical leadership role designed for a seasoned expert who combines deep mastery of classical Machine Learning, predictive modeling, and supply chain/commercial analytics with cutting-edge, enterprise-grade Generative AI and Agentic workflows.

You will tackle complex, enterprise-scale challenges across commercial operations, global supply chain, pricing strategy, and customer experience ecosystems.

Important Note: This is not a reporting, dashboarding, or business intelligence role. We are looking for an execution-focused practitioner who owns the complete lifecycle of data science initiativesfrom ambiguous problem definition and architecture to production deployment, MLOps, and direct alignment with commercial ROI.

Key Responsibilities

1. Classical Data Science, Predictive Modeling & Commercial Analytics

• Architect, build, train, and deploy high-performance machine learning models to solve complex B2B commercial, pricing, and operational challenges.
• Apply advanced supervised and unsupervised paradigms, including regression, classification, clustering, price-elasticity, demand modeling, and time-series forecasting.
• Drive advanced exploratory data analysis (EDA), rigorous feature engineering, and robust model validation to prevent data leakage and train-serve skew.
• Engineer and optimize deep learning and natural language processing (NLP) architectures using TensorFlow and PyTorch.

2. Generative AI, RAG & Agentic Systems

• Design and scale enterprise-grade Generative AI solutions as an extension of core data science platforms.
• Construct advanced LLM-powered workflows utilizing LangChain, LangGraph, and HuggingFace.
• Architect semantic search and retrieval systems using advanced chunking, hybrid retrieval, embeddings, and vector databases (e.g., FAISS, Pinecone, pgvector).
• Implement intelligent multi-agent systems and tool-calling frameworks (e.g., CrewAI, AutoGen) to automate complex research and decision-support tasks.

3. Enterprise Data, Cloud & MLOps Engineering

• Work seamlessly with structured, unstructured, and multi-source enterprise data pipelines.
• Collaborate with data engineering teams to ensure robust, scalable pipelines using PySpark, Databricks, and cloud platforms (AWS, GCP, or Azure).
• Oversee full-lifecycle MLOps, including containerization (Docker, Kubernetes), experiment tracking (MLflow), model deployment, and real-time drift monitoring.

4. Strategic Collaboration & Technical Leadership

• Partner closely with product managers, business stakeholders, and C-suite leaders to translate ambiguous business challenges into scalable technical roadmaps.
• Articulate complex data insights, model trade-offs, and strategic financial impacts to both technical and non-technical audiences.
• Provide technical mentorship, code reviews, and governance oversight to junior data scientists and engineers.

Required Technical Skill Set

• Core Language & Libraries: Python (Advanced), R, PySpark, Pandas, NumPy, Scikit-learn, SQL.
• Classical ML & Deep Learning: Supervised & Unsupervised Learning, Regression, Classification, Clustering, Time-Series Modeling, Deep Learning, NLP, TensorFlow, PyTorch.
• Generative & Advanced AI: LangChain, LangGraph, HuggingFace, BERT, Embeddings, Vector Databases (FAISS, Pinecone, pgvector), Multi-Agent Systems (CrewAI, AutoGen), Azure OpenAI / Vertex AI.
• Cloud, Big Data & MLOps: Azure ML, Databricks, AWS (S3, Lambda), GCP, Docker, Kubernetes, MLflow, CI/CD pipelines.

Education & Experience Requirements

• Experience: 10 to 18+ years of professional experience with a proven track record of delivering end-to-end AI/ML solutions that drive measurable business impact (revenue growth, cost reduction, or operational efficiency).
• Education: Bachelors degree in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field. A Master’s degree (M.Tech, PGP, PGD, or MS in Business Analytics/Data Science) from a premier institution (such as IIT, BITS Pilani, or ISB) is strongly preferred.
• Work Style: Demonstrated success operating with strong ownership—capable of driving complex projects autonomously as an Individual Contributor while steering cross-functional technical execution.