Lead Data Scientist

About the Role

Key responsibilities include understanding business problems, analyzing data, defining success criteria, and collaborating with engineering and architecture teams for data identification, harmonization, and cleansing. You will select appropriate algorithms, enhance models with additional data inputs, build interpretable and scalable solutions, and present visualizations to stakeholders, all within Agile methodologies and a production environment. You are expected to explore and recommend new tools and processes for the data preparation pipeline, ensure tight integration of development and deployment, and curate code and binary artifact repositories. Collaboration with AI strategists, DevOps, and domain experts will drive model performance, while you communicate findings through storytelling and apply advanced machine‑learning and statistical techniques, including Databricks and deep‑learning tools where relevant. • Define business problems, analyze data, and set success criteria. • Collaborate with engineering and architecture to identify, harmonize, and cleanse data. • Select and implement appropriate algorithms; enhance models with additional data inputs. • Build interpretable, scalable models and create visualizations for stakeholders. • Follow Agile practices and deliver production‑ready machine‑learning products. • Recommend and integrate new tools and processes across data pipelines. • Curate code and binary artifact repositories; ensure seamless deployment experience. • Partner with AI strategists, DevOps, and domain SMEs to improve model performance. • Research and troubleshoot complex data challenges, leveraging Databricks and deep‑learning tools. • Design end‑to‑end ML pipelines covering data processing, feature engineering, training, evaluation, deployment, and monitoring. • Implement model monitoring, explainability, versioning, and AI governance.

Key Responsibilities

• ▸data analysis
• ▸model development
• ▸ml pipelines
• ▸model monitoring
• ▸tool integration
• ▸stakeholder visualization

What You Bring

The Lead Data Scientist role is built for individuals who excel at designing new technologies, diving deep into data, and optimizing digital experiences. You will thrive in a performance culture that values sustainability, inclusion, wellbeing, and career development while contributing to better care for billions of people worldwide. Qualifications include at least 10 years of experience building scalable, multi‑tenant ML models in cloud environments (AWS, Azure, GCP) for supply chain, sales, marketing, and other business areas. Proficiency in Python, SQL, Docker, Kubernetes, and ML platforms (Azure Machine Learning, SageMaker, MLflow, KubeFlow) is required, along with expertise in end‑to‑end AI lifecycle, model monitoring, explainability, CI/CD, and Agile development. • Communicate insights through effective storytelling to leadership. • 10+ years experience building scalable ML models in cloud environments (AWS, Azure, GCP). • Expert in Python, SQL, Docker, Kubernetes, and ML platforms (Azure ML, SageMaker, MLflow, KubeFlow). • Strong knowledge of CI/CD, source code management, microservices, and REST APIs. • Self‑starter able to prioritize high‑impact work and drive innovation.

Requirements

• ▸10+ years
• ▸python
• ▸sql
• ▸docker
• ▸kubernetes
• ▸ci/cd

Benefits

The ideal candidate performs at the highest level, embraces a caring performance culture, and is committed to sustainability, inclusion, and personal wellbeing. You will benefit from flexible hybrid work arrangements that balance purposeful office time with personal flexibility, while upholding safety, mutual respect, and human dignity. To apply, click the Apply button and complete the online application; a recruiter will review your submission. Kimberly‑Clark is an equal‑opportunity employer that values diverse experiences and fosters an inclusive workforce. The role is based in Bengaluru (IT Centre) with hybrid work options, full‑time employment, and a commitment to sustainable, innovative practices. • Flexible hybrid work arrangement supporting work‑life balance.

Work Environment

Office Full-Time