About Kenvue
At Kenvue, we realize the extraordinary power of everyday care. Built on over a century of heritage and rooted in science, we’re the house of iconic brands - including NEUTROGENA®, AVEENO®, LISTERINE®, JOHNSON’S® and BAND-AID® that you already know and love. Science is our passion; care is our talent. Our global team is made by 22,000 diverse and brilliant people, passionate about insights, innovation and committed to deliver the best products to our customers. With expertise and empathy, being a Kenvuer means to have the power to impact life of millions of people every day. We put people first, care fiercely, earn trust with science and solve with courage – and have brilliant opportunities waiting for you! Join us in shaping our future–and yours.
The Digital Transformation Office is orchestrating our journey to become a Digital First organization by building and deploying end-to-end digital solutions that solve unmet needs and generate value through a fully integrated digital ecosystem.
About the Role
The Data Scientist will build scalable solutions for Supply Chain teams. This individual will work with cross-functional subject matter experts (SMEs) to deeply understand business context and key business questions / opportunities, as well as collaborate with other teams (e.g., Digital Capability teams, PMO, IT delivery, Data Engineering, Data Quality, Data Management & Governance, ML Ops) to ensure delivery of scalable data science solutions.
The ideal candidate for this role will demonstrate a combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of the project and excellent written and communications skills to report back the findings in a clear, structured manner.
Key Responsibilities
- Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions.
- Create Proof of concepts (POCs) / Minimum Viable Products (MVPs), then guide them through production deployment and operationalization of projects.
- Influence machine learning strategy for Digital programs and projects.
- Make solution recommendations that appropriately balance speed to market and analytical soundness.
- Explore design options to assess efficiency and impact, develop approaches to improve robustness and rigor.
- Develop analytical / modelling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow).
- Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations.
- Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
- Create algorithms to extract information from large, multiparametric data sets.
- Deploy algorithms to production to identify actionable insights from large databases.
- Compare results from various methodologies and recommend optimal techniques.
- Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
- Develop and embed automated processes for predictive model validation, deployment, and implementation.
- Work on multiple pillars of AI including cognitive engineering, conversational bots, and data science.
- Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment.
- Provide guidance and leadership to more junior data scientists, managing processes and flow of work, vetting designs, and mentoring team members to realize their full potential.
- Lead discussions at peer review and use interpersonal skills to positively influence decision making.
- Provide thought leadership and subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices.
- Facilitate cross-geography sharing of new ideas, learnings, and best-practices.
Required Qualifications
- Bachelor of Science or Bachelor of Engineering at a minimum.
- 4+ years of work experience as a Data Scientist.
- A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of a project.
- Advanced skills with statistical/programming software (e.g., R, Python) and data querying languages (e.g., SQL, Hadoop/Hive, Scala).
- Good hands-on skills in both feature engineering and hyperparameter optimization.
- Experience producing high-quality code, tests, documentation.
- Experience with Microsoft Azure or AWS data management tools such as Azure Data factory, data lake, Azure ML, Synapse, Databricks.
- Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and / or deep learning methodologies.
- Proficiency in statistical concepts and ML algorithms.
- Good knowledge of Agile principles and process.
- Ability to lead, manage, build, and deliver customer business results through data scientists or professional services team.
- Ability to share ideas in a compelling manner, to clearly summarize and communicate data analysis assumptions and results.
- Self-motivated and a proactive problem solver who can work independently and in teams.
Desired Qualifications
- Experience working in one or multiple supply chain functions (e.g., procurement, planning, manufacturing, quality, logistics) is strongly preferred.
- Experience in applying AI/ML within a CPG or Healthcare business environment is strongly preferred.
- Exposure to Pyomo, Mosel, truck load optimization, multi-echelon inventory optimization (MEIO).
- Experience in NLP, Vision, and/or AR / VR.
- Experience in creating CI/CD pipelines for deployment using Jenkins.
- Experience implementing MLOPs framework along with understanding of data security implementation on ML models.
- Hands on experience developing ML driven models.