Senior Principal Data Scientist

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

This role is open to candidates in Plano, TX and Purchase, NY.

PepsiCo Data & Analytics Overview:

With data deeply embedded in our DNA, PepsiCo Data & Analytics transforms data into consumer delight. We build and organize business-ready data that allows PepsiCo’s leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise.
The Data Science Pillar in D+A will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within D+A Products. And will support “pre-engagement” activities as requested and validated by the prioritization framework of D+A.

Position Overview:

• We are hiring for a few different teams including our cDNA & ROI teams on the Commercial side, as well as our NRM team.
• Your role will be to be part of growing team focused on the commercial aspect of data science. You’ll create and support global digital developments for PepsiCo. These developments will be around topics like revenue management, assortment optimization, marketing mix modeling, marketing audience selections, sales effectiveness, prescriptive analytics, in market execution, and more.
• You will be part of a collaborative interdisciplinary team around data, where you will be responsible of building deployable statistical/machine learning models, starting from the discovery phase. You will work closely with process owners, product owners and final business users. This will provide you the correct visibility and understanding of criticality of your developments.
• You will be an internal ambassador of the team’s culture around data and analytics. You will provide stewardship to colleagues in the areas that you are a specialist or in which you are specializing.

Primary Accountabilities:

• Lead other team members in the different digital projects acting as tech lead for the project.
• Act as a subject matter expert across different digital projects.
• Act as stream leader in innovation activities
• Partner with product managers in taking DS requirements and assessing DS components in roadmaps.
• Partner with data engineers to ensure data access for discovery and proper data is prepared for model consumption.
• Partner with ML engineers working on industrialization.
• Coordinate work activities with Business teams, other IT services and as required.
• Drive the use of the Platform toolset and to also focus on 'the art of the possible' demonstrations to the business as needed.
• Communicate with business stakeholders in the process of service design, training and knowledge transfer.
• Support large-scale experimentation and build data-driven models.
• Set KPIs and metrics to evaluate analytics solution given a particular use case.
• Refine requirements into modelling problems.
• Influence product teams through data-based recommendations.
• Research in state-of-the-art methodologies.
• Create documentation for learnings and knowledge transfer.
• Create reusable packages or libraries.

Qualifications/Requirements

cDNA (Customer/Consumer Focused Data) Team Qualifications:

• Technical skills/ML understandings such as clustering/dimension reduction/regression, big data handling (PySpark), and strong business communication/data interpretation.
• Leadership experience and skills
• Demonstrated ability to effectively and concisely communicate with both business and technical audiences

ROI Team Qualifications:

• Master’s Degree in Data Science or equivalent, Ph.D. Preferred
• 10 years of relevant work experience, 6 years with a Ph.D.
• At least 4 years of software development and machine learning experience in a business environment
• Experience leading technical teams
• Expert in mathematical models underlying data science methods
• Demonstrated ability to effectively and concisely communicate with both business and technical audiences

General Qualifications:

• 8+ years’ experience building solutions in the commercial or in the supply chain space.
• 8+ years working in a team to deliver production level analytic solutions. Fluent in git (version control). Understanding of Jenkins, Docker are a plus.
• 8+ years’ experience in ETL and/or data wrangling techniques. Fluent in SQL syntaxis.
• 8+ years’ experience in Statistical/ML techniques to solve supervised (regression, classification) and unsupervised problems. Experiences with Deep Learning are a plus.
• 8+ years’ experience in developing business problem related statistical/ML modeling with industry tools with primary focus on Python or Scala development.
• Business storytelling and communicating data insights in business consumable format. Fluent in one Visualization tool.
• Strong communications and organizational skills with the ability to deal with ambiguity while juggling multiple priorities.
• Experience with Agile methodology for teamwork and analytics ‘product’ creation (e.g., Jira, Confluence).
• Experience with cloud services is a must, Azure is a plus.
• Experience in Reinforcement Learning is a plus.
• Experience in Simulation and Optimization problems in any space is a plus.
• Experience with Bayesian methods is a plus.
• Experience with Causal inference is a plus.
• Experience with NLP is a plus.
• Experience with working with FAIR data is a plus.
• Experience with Responsible AI is a plus.
• Experience with distributed machine learning is a plus

Competencies:

• Creative and collaborative mindsets.
• Translates complex technical solutions into business understanding.
• Creates solutions with the ability to continually prove accuracy and with a design goal of productization/ reusability.