Data Science Manager

Unilever · Consumer Goods (FMCG)

Uniops Bangalore Centre
On-site
Posted today
Apply by 2 Nov
Data & AI

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About the job

Job Title : Data Science Manager – CD XOPS

Location : Bangalore

About Unilever

Unilever is one of the world’s leading suppliers of Food, Home, and Personal Care products, operating in over 190 countries and reaching more than 2 billion consumers every day. Our portfolio includes iconic brands such as Dove, Knorr, Domestos, Hellmann’s, Persil, Cif, Tresemmé, Rexona, and Axe.

Guided by our purpose—to make sustainable living commonplace—we aim to grow our business while addressing the challenges of climate change and human development, enabling people everywhere to live well within the limits of the planet.

About Global Data & Technology (GDT)

Data Foundation within GDT exists to make Unilever data-intelligent, empowering critical business decisions through data, advanced analytics, and AI.

Our vision is a future-fit Unilever—an organisation where data underpins decision-making across all functions: accelerating innovation, strengthening brands, driving excellence in customer execution, enhancing consumer experiences through personalisation, and transforming internal operations for efficiency and scale.

About the Role

As a Data Science Manager within the Customer Development XOPS team in Data Foundation, you will be responsible for leading data science delivery across products and markets, shaping technical direction, and developing high-performing teams. This is a hands-on leadership role, combining technical depth with people management and strong business partnership.

You will own the end-to-end data science lifecycle for key initiatives—translating business problems into scalable analytics solutions, ensuring high-quality delivery, and embedding data science into decision-making at scale.

Key Responsibilities

Technical & Product Leadership

Own and shape the data science roadmap for products within our Customer Development (CD) portfolio, aligned to commercial priorities and business outcomes.

Lead the design, development, and deployment of diagnostic, predictive, and prescriptive analytics solutions, ensuring robustness, scalability, and interpretability.

Set modelling and analytical standards across the team, covering classical statistical methods, machine learning, and emerging Generative and Agentic AI capabilities.

Ensure solutions are industrialised and production-ready, leveraging cloud platforms (Azure, Databricks) and aligned with MLOps best practices.

People Leadership & Capability Building

Lead, coach, and develop a team of data scientists, supporting both technical growth and career progression.

Foster a culture of high ownership, continuous improvement, and learning, encouraging experimentation while maintaining delivery discipline.

Provide technical guidance and review, acting as a mentor and escalation point for complex analytical challenges.

Stakeholder & Business Partnership

Partner closely with Product, Engineering, and Business stakeholders to shape problem definitions, prioritise initiatives, and drive adoption of data science solutions.

Translate complex analytics into clear, actionable insights for senior stakeholders, supporting data-driven decision-making.

Balance short-term delivery with long-term capability building, ensuring alignment between local market needs and global product strategy.

Ways of Working & Continuous Improvement

Champion agile ways of working, enabling fast-paced experimentation and iterative delivery.

Identify opportunities to build reusable frameworks, scale solutions, and feed innovation into the global CD product pipeline.

About You

Experience & Expertise

Degree qualified in a relevant technical discipline (Data Science, Computer Science, Engineering, Mathematics, Statistics, Econometrics, or similar).

Experience in delivering complex analytics initiatives in a commercial or product-driven environment.

Strong background in data science modelling, with deep expertise in several of the following:

o Time series forecasting

o Econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space

o Simulation and optimisation methodologies and tools

Experience in design and development of agentic tools such as SQL assistants, Retrieval-Augmented Generation (RAG) pipelines, and multi-agent orchestration frameworks (e.g. LangGraph, ADK, or similar).

Exposure to designing and industrialising machine learning solutions at scale is a strong advantage.

Advanced proficiency in Python, Spark, and modern analytics stacks; hands-on experience with Databricks and Azure.

Leadership & Mindset

Strong people leader with experience coaching and developing talent in multidisciplinary teams.

Excellent communicator, able to influence and align senior stakeholders through clear storytelling and data-driven narratives.

Strategic thinker with the ability to prioritise effectively and focus teams on what delivers the most value.

Comfortable operating in a fast-paced, global, and ambiguous environment, balancing delivery with longer-term vision.

High ethical standards in data usage, governance, and decision-making.

What We Offer

A leadership role shaping high-impact, global data science products.

The opportunity to build and lead teams working on cutting-edge analytics, AI, and cloud technologies.

A collaborative, inclusive culture that values ownership, learning, and innovation.

Direct contribution to Unilever’s growth ambitions and sustainability purpose.

Our commitment to Equality, Diversity & Inclusion

Unilever embraces diversity and encourages applicants from all walks of life! This means giving full and fair consideration to all applicants and continuing development of all employees regardless of age, disability, gender reassignment, race, religion or belief, sex, sexual orientation, marriage and civil partnership, and pregnancy and maternity.

Note: "All official offers from Unilever are issued only via our Applicant Tracking System (ATS). Offers from individuals or unofficial sources may be fraudulent—please verify before proceeding."

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