Data Scientist, Manager

Ready to build the future with AI?

At Genpact, we don’t just keep up with technology—we set the pace. AI and digital innovation are redefining industries, and we’re leading the charge. Genpact’s AI Gigafactory, our industry-first accelerator, is an example of how we’re scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies’ most complex challenges. 

If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what’s possible, this is your moment. 

Genpact (NYSE: G) is anadvanced technology services and solutions company that deliverslastingvalue for leading enterprisesglobally.Through ourdeep business knowledge, operational excellence, and cutting-edge solutions – we help companies across industries get ahead and stay ahead.Powered by curiosity, courage, and innovation,our teamsimplementdata, technology, and AItocreate tomorrow, today.Get to know us atgenpact.comand onLinkedIn,X,YouTube, andFacebook. 

Inviting applications for the role of Data Scientist, Manager

Data Scientist – Promotion Analytics will support the Store Operations pillar of the Enterprise Analytics Team. Reporting directly to the internal team of data science managers, we are looking for candidates who understand the foundational aspect of Data Science and will apply Data Science principles and technology to improve member acquisition, engagement, and retention

Role: Data Scientist, Manager

Responsibilities

• Under the direct supervision of internal Data Science managers, formulate solutions to problems related to membership acquisition, retention and engagement as well as digital and operations

  
  

• Be able to independently frame business problems and decompose their constituent parts to determine proper data science solutioning

  
  

• Build machine learning models and use applied statistics (supervised/unsupervised learning, lift modeling, linear/nonlinear programming, feature selection) to support the business

  
  

• Enhance existing and build new technology solutions in support of business priorities

  
  

• Use proper statistical techniques to provide ad hoc data science/EDA support when needed

  
  

• Be a thought partner in the creation of our testing plans and provide “next level” insights related to the measurement of tests

  
  

• Certify all testing/measurement (hypothesis testing, A/B testing, design of experiment, sample size/power calculation)

  
  

• Communicate complex data science concepts/insights to non-technical audiences

  
  

• Participate in agile work planning—projects will be selected from use cases across the enterprise

  
  

• Drive continuous improvement by evaluating and implementing new data science methodologies, tools, and frameworks.

  
  

Qualifications we seek in you!

Minimum Qualifications

• BSc in Computer Science, engineering (or related area), Statistics, Mathematics, or equivalent experience

  
  

• Experience in developing machine learning models for retail use cases

  
  

• Experience in crafting ad hoc data mining analysis for retail use cases

  
  

• Experience in algorithms, data structures, and object-oriented programming

  
  

• Experience on engineering and development collaboration tool such as Jira/Confluence/Git

  
  

• Experience in mentoring and delegating work to junior analysts, involves providing essential business context, offering guidance on bug fixes, and conducting thorough reviews of their work

  
  

• Implementation experience in building machine learning algorithms and applications, deploying and maintaining NLP models for retail use cases

  
  

• Experience in time-series forecasting, clustering algorithms, and dimensionality reduction techniques

  
  

• Strong programming skills in at least one object-oriented programming language (Python, PySpark, SQL, Java)

  
  

• Experience with cluster-computing frameworks (e.g. Spark, Hadoop)

  
  

• Expertise in configuring and managing large, complex data pipelines using AWS services (e.g., Glue, SageMaker, Lambda), with experience in GCP/Azure as a plus