Location: Hyderabad, India (Aurobindo Galaxy, Knowledge City)

Position Type: Full-Time | Permanent Position

Work Mode: Hybrid

Experience: 8 – 15 Years+

Company: Solenis GSS India Pvt Ltd

Solenis is a leading global producer of specialty chemicals focused on delivering sustainable solutions for water-intensive industries. Our Global Systems and Services (GBS) hub in Hyderabad drives innovation, digital transformation, and enterprise operational excellence worldwide.

Solenis GSS India is looking for a highly skilled Senior Data Scientist to join our AI & Data Science team. This role is designed for a strong individual contributor who brings deep expertise in classical Data Science and Machine Learning, with selective and practical exposure to Generative AI.

You will work on complex, enterprise‑scale problems, building and deploying end‑to‑end machine learning and AI solutions that drive measurable business impact across commercial operations, supply chain, and customer experience domains.

Note: This role is not focused on reporting, dashboards, or pure analytics. We are seeking hands‑on practitioners who own the full lifecycle of data science solutions—from problem definition to production deployment.

Data Science & Machine Learning

• Design, build, train, and deploy machine learning models to solve complex business problems.

  
  

• Apply supervised and unsupervised learning techniques including regression, classification, clustering, and time‑series forecasting.

  
  

• Perform advanced exploratory data analysis (EDA), feature engineering, and model validation.

  
  

• Develop and optimize deep learning and NLP models using frameworks such as TensorFlow and PyTorch.

  
  

Generative AI & Advanced AI

• Apply Generative AI techniques as an extension of core Data Science solutions.

  
  

• Build LLM‑powered workflows using frameworks such as LangChain and HuggingFace.

  
  

• Work with embeddings, vector databases, and tools such as FAISS for intelligent search and retrieval use cases.

  
  

• Collaborate on AI agent‑based solutions using tools like CrewAI or AutoGen where applicable.

  
  

• Ensure GenAI solutions are production‑ready, scalable, and aligned with business needs.

  
  

Enterprise Data & Production Systems

• Work with structured and unstructured data across enterprise platforms.

  
  

• Collaborate with data engineering teams to ensure robust and scalable data pipelines.

  
  

• Develop solutions deployed on cloud platforms such as AWS, GCP, or Azure.

  
  

• Contribute to model deployment, monitoring, and optimization in production environments.

  
  

Collaboration & Communication

• Partner with product managers, engineers, and business stakeholders to translate business problems into data science solutions.

  
  

• Communicate insights, model outcomes, and recommendations clearly to both technical and non‑technical audiences.

  
  

• Provide technical guidance and mentorship to junior team members when required.

  
  

Education

• Bachelor’s degree in computer science, Data Science, Statistics, Engineering, or a related field.

  
  

• Master’s degree such as M.Tech / PGP / PGD in Data Science or Business Analytics preferred.

  
  

• Coursework completed in 2017 or earlier is strongly preferred.

  
  

Core Technical Skills

• Programming: Strong proficiency in Python and SQL.

  
  

• Libraries: Hands‑on experience with Pandas, NumPy, and Scikit‑learn.

  
  

• ML Methodologies: Deep understanding of Supervised & Unsupervised Learning (Regression, Classification, Clustering) and Time Series Modeling.

  
  

• Deep Learning & NLP: Solid experience with Natural Language Processing (NLP) using frameworks like TensorFlow and/or PyTorch.

  
  

Advanced / GenAI Skills

• Practical experience with LangChain, Llama Index, HuggingFace, and BERT.

  
  

• Working knowledge of Embeddings & Vector Databases (e.g., FAISS).

  
  

• Professional exposure to cloud environments (AWS, GCP, or Azure) alongside Azure OpenAI / Vertex AI configurations.

  
  

Experience Requirements

• 8–15 years of overall experience with strong hands‑on Data Science and ML ownership.

  
  

• Proven track record of delivering end‑to‑end ML solutions in enterprise environments.

  
  

• Deep experience working as an Individual Contributor on complex technical problems.

  
  

Preferred Attributes

• Direct exposure to industrial, manufacturing, or enterprise commercial business domains.

  
  

• Experience working with large‑scale enterprise data platforms.

  
  

• Familiarity with responsible AI practices, model governance, and data quality frameworks.