Customer Analytics & Insights Manager at Fil · Gurugram

Customer Analytics & Insights Manager Location: Gurgaon Office About the Opportunity Job Type: Permanent Application Deadline: 16 May 2026 Job Description Title                     Customer Analytics & Insights Manager Department         Global Central Marketing Operations - Customer Insights & Analytics Location              Gurugram Reports To          Customer Analytics & Insights Senior Manager Level                    Manager - Grade 5 Our clients come from all walks of life and so do we.  We are proud of our inclusive culture and encourage applications from the widest mix of talent, whatever your age, gender, ethnicity, sexual orientation, gender identity, social background and more. As a flexible employer, we trust our people to perform their role in the way that works best for them, our clients and our business. We are a disability-friendly company and would welcome a conversation with you if you feel you might benefit from any reasonable adjustments to perform to the best of your ability during the recruitment process and beyond. Find out more about what we do, our history, and how you could be a part of our future at careers.fidelityinternational.com/about-us. Department Description The Global Central Marketing Operations organisation is responsible for designing, building, optimising and measuring multi-channel customer journeys across Email, SMS/WhatsApp, Push, In‑App, and Web experiences. Partnering closely with Data, MarTech, and Brand teams, we use our Orchestration Engine to activate first‑party data responsibly and at scale, ensuring communications are timely, relevant and compliant. About the team The Customer Analytics & Insights team transforms customer data into actionable insights that optimize multi-channel journeys. Providing measurement, segmentation, and experimentation frameworks to improve engagement, conversion, and retention. Key Responsibilities Use machine learning tools and statistical techniques to produce solutions to problems Use advances in data science to create forecasting and propensity models to predict the future behaviour of our customers Test data mining models to select the most appropriate ones for use on a project Working closely with the Data Engineer, Data Scientists, Data Architects and Internal stakeholders to drive the data science projects Building scalable machine learning pipelines and using feature engineering and optimisation methods to improve data set performance Increase our understanding of our customer bases by ensuring we have greater transparency on our customer base, their demographics and behaviours Support or drive marketing and technology initiatives Actively develop new cloud-productionised prediction models with outputs Manage dashboards and output of models effectively to drive customer insight and campaign targeting Experience and Qualifications Proven experience as a Data Scientist, Machine Learning Engineer, or similar analytical role. Strong proficiency in Python, SQL, and statistical modeling techniques. Experience with machine learning frameworks (e.g., scikit‑learn, TensorFlow, PyTorch). Comfort working with large datasets, cloud environments, and modern data stacks. Experience in marketing analytics, experimentation, or customer analytics is preferred. Ability to communicate complex technical topics to non‑technical stakeholders. Strong problem-solving skills, curiosity, and a hypothesis driven approach.‑solving skills, curiosity, and a hypothesis‑driven approach. About you 8+ years of technical expertise in data models, data mining, and segmentation techniques Proficiency in designing algorithms and using statistical and problem-structuring methods Ability to use linear and non-linear regression, logistic regression, models and classification techniques for data analysis, clustering, dimensionality reduction, k-NN and pipeline Strong experience in using visualisation tools for large-scale data dashboarding e.g. Power BI, Tableau Strong experience using Python and its libraries specialising in Data Science and Data Engineering capabilities e.g. Pandas, Sklearn, pyspark, matplotlib etc.