Senior Digital Innovation Engineer - Data Science

Senior Digital Innovation Engineer - Data Science

Digital Chennai, India Chetpet

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Description

Senior Digital Innovation Engineer – Data Science

Location

Chennai, India

Required Language

English

Employment Type

Full Time

Seniority Level

Mid – Senior level

Travel

10 %

Buckman is a privately held, global specialty chemical company with headquarters in Memphis, TN, USA, committed to safeguarding the environment, maintaining safety in the workplace, and promoting sustainable development. Buckman works proactively and collaboratively with its worldwide customers in pulp and paper, leather, and water treatment to deliver exceptional service and innovative specialty chemical solutions to help boost productivity, reduce risk, improve product quality, and provide a measurable return on investment. Buckman is in the middle of a digital transformation of its businesses and focused on building the capabilities and tools in support of this.

Role Summary

We are seeking a Senior Data Scientist with strong hands-on experience in building, deploying, and scaling machine learning and deep learning solutions on cloud platforms (preferably Azure). The ideal candidate will have practical experience working with Large Language Models (LLMs)—either as chatbots, copilots, or agentic AI systems—and a proven track record of delivering production-grade AI solutions. We are looking for someone who is capable of owning end-to-end AI solutions, mentoring team members, and collaborating with global stakeholders to create measurable business impact.

Basic Qualifications (Must-Have)

Bachelor’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related quantitative field

7+ years of hands-on experience in data science, machine learning, or applied AI roles

Strong experience in

Designing, training, and deploying ML/DL models into production

Cloud-based ML workflows, preferably on Microsoft Azure

Python-based ML ecosystem (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow, etc.)

Proven experience with

End-to-end ML lifecycle (data ingestion → modeling → deployment → monitoring)

Model performance evaluation, retraining strategies, and production stability

Demonstrated success through

Production deployments, internal tools, or a verifiable portfolio of AI problems solved

Preferred Qualifications (Strong Advantage)

Master’s or PhD degree in Data Science, Computer Science, AI, Statistics, Engineering, or related field

Hands-on experience with Generative AI / LLMs, including

Chatbots, copilots, or conversational AI systems

Agentic architectures (tool calling, memory, orchestration, RAG pipelines, multi-step reasoning)

Experience using or deploying

Azure OpenAI, OpenAI, or similar LLM platforms

Vector databases, embeddings, prompt engineering, and retrieval-based systems

Experience with MLOps / LLMOps, including

CI/CD for ML

Model versioning, monitoring, and observability

Azure ML, Azure DevOps, or similar platforms

Prior exposure to

Specialty chemicals, manufacturing, supply chain, logistics, or industrial analytics

Engineering background (especially Chemical Engineering)

Experience

Setting up or scaling a data science program

Working with global or cross-functional stakeholders

Working in a startup or fast-paced product environment

Leadership & Ownership (Added Emphasis)

Experience mentoring junior data scientists or ML engineers

Ability to own AI initiatives end-to-end, from problem framing to business impact

Experience influencing technical decisions, architecture, or best practices

Comfort working independently while collaborating with product, IT, and business teams

Core Responsibilities

Design, build, and deploy production-grade ML/DL and GenAI solutions

Lead development of LLM-based applications, including chatbots and agentic workflows

Architect scalable cloud-based AI solutions using Azure services

Partner with business stakeholders to translate real-world problems into AI solutions

Ensure reliability, performance, and governance of AI systems in production

Contribute to best practices in ML engineering, GenAI architecture, and MLOps

Support adoption, documentation, and long-term maintainability of AI platforms

Personality Traits & Soft Skills

Strong business mindset with a bias for execution and measurable impact

High ownership and accountability for outcomes, not just models

Excellent collaboration skills across data, engineering, and business teams

Clear communicator with the ability to explain complex AI concepts to non-technical audiences

Strong planning and organizational skills; able to manage multiple initiatives

Continuous learner, especially in fast-evolving areas like GenAI and agentic AI

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