Analytics and Data Science Trainee at Kimberly-Clark

pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

21 Jul, 26

Salary

0.0

Posted On

22 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data science, Machine learning, Data cleansing, Power BI, Data modeling, Dashboards, Reporting, LLM workflows, Data ingestion, Model training, Model validation, Analytical solutions, Data-driven decision-making, Cross-functional collaboration, Communication skills, Problem solving

Industry

Manufacturing

Description

Analytics and Data Science Trainee Job Description Join the team behind iconic brands like Huggies®, Kleenex®, Cottonelle®, Scott®, Kotex®, Poise®, Depend®, and Kimberly-Clark Professional®. At Kimberly-Clark, it’s all here for you—innovation, growth, and the chance to make a real impact. About You In this Professional role, you’ll focus on supporting accurate and compliant intercompany accounting activities while partnering with Global Business Services and internal stakeholders. You’ll contribute to timely execution of routine transactions, reporting, and issue resolution that supports Kimberly‑Clark’s financial integrity and control environment. In this role, you’ll help us deliver better care for billions of people around the world. It starts with YOU. In this role, you’ll support GBS and Enterprise teams (via GBS) by delivering end‑to‑end analytical solutions that enable data‑driven decision‑making. You bring a strong analytical mindset and hands‑on experience in data science, machine learning, and data cleansing, and enjoy working with complex datasets to generate meaningful insights. You thrive in a collaborative, fast‑paced environment and are comfortable partnering with cross‑functional stakeholders to deliver scalable, high‑impact solutions. Business Priorities and Key Accountabilities In this role, you will: Provide end‑to‑end analytical support for GBS and Enterprise projects (via GBS) with a focus on data science, machine learning, and data cleansing. Apply advanced analytics and machine learning techniques to solve complex business problems and support enterprise priorities. Partner closely with stakeholders to understand analytical requirements and translate them into scalable data solutions. Ensure data quality and reliability through effective data preparation, validation, and ongoing model monitoring. Communicate insights clearly through structured analysis, visualizations, and reporting. Functional / Business Skills To be successful in this role, you will bring: Experience building, training, and deploying machine learning (ML) systems. Strong capabilities in data ingestion, model training, and ongoing model validation. Experience designing and building LLM‑based workflows. Extensive hands‑on expertise in Power BI (PBI), including data modeling, dashboards, and reporting. Essential Knowledge / Experience To succeed in this role, you will need the following: Bachelor’s degree in computer science or a related quantitative field, with a focus on data science or analytics. Strong oral and written communication skills, with high attention to detail. Ability to work through challenging situations or complex problems to achieve goals. Demonstrated ability to manage and prioritize multiple projects while providing clear stakeholder updates and communications. Ability to collaborate effectively with a diverse set of colleagues, customers, and stakeholders. To Be Considered Click the Apply button and complete the online application process. A member of our recruiting team will review your application and follow up if you seem like a great fit for this role. In the meantime, please check out the careers website. And finally, the fine print… For Kimberly-Clark to grow and prosper, we must be an inclusive organization that applies the diverse experiences and passions of its team members to brands that make life better for people all around the world, which is why we seek to build a workforce that encompasses the experiences of our consumers. When you bring your original thinking to Kimberly-Clark, you fuel the continued success of our enterprise. We are a committed equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, national origin, disability status, protected veteran status, sexual orientation, gender identity, age, pregnancy, genetic information, citizenship status, or any other characteristic protected by law. The statements above are intended to describe the general nature and level of work performed by employees assigned to this classification. Statements are not intended to be construed as an exhaustive list of all duties, responsibilities and skills required for this position. This role is available for local candidates already authorized to work in the role’s country only. Kimberly-Clark will not provide relocation support for this role. #LI-Hybrid Primary Location Pune Kharadi Hub Additional Locations Worker Type Employee Worker Sub-Type Fixed Term (Fixed Term) Time Type Full time Fueled by ingenuity, creativity, and an understanding of people's most essential needs, we're working to find new ways to make a positive impact on the world we share. Kimberly-Clark and its trusted brands, including Huggies, Kleenex, Scott, Kotex, Cottonelle, Poise, Depend, Andrex, Pull-Ups, GoodNites, Intimus, Neve, Plenitud, Sweety, Softex, Viva and WypAll, are an indispensable part of life for people in more than 175 countries by helping individuals experience more of what's important to them. We use sustainable practices that support a healthy planet, build stronger communities, and ensure our business thrives for decades to come. To learn more about the company's 150-year history of innovation, visit kimberly-clark.com.

Responsibilities

Provide end-to-end analytical support for GBS and Enterprise projects with a focus on machine learning and data science. Partner with stakeholders to translate analytical requirements into scalable data solutions while ensuring data quality and reliability.