Data Science Associate Manager

Location: New York, New York, United States

Overview We’re seeking a Data Science Manager to lead a high-performing team delivering scalable analytics and machine learning solutions. In this role, you will drive the technical vision, execution, and adoption of data science initiatives that support our global Strategy and Transformation agenda. You will partner closely with business and technology leaders to identify high-impact opportunities and foster a strong data-driven culture. Responsibilities Lead, mentor, and grow a team of data scientists, providing technical guidance and career development Own the end-to-end delivery of large-scale analytics and machine learning initiatives Define standards and best practices for model development, data pipelines, and ML production workflows Oversee the design, deployment, monitoring, and optimization of machine learning systems in production Partner with business stakeholders to translate strategic goals into data science roadmaps and measurable outcomes Drive adoption of modern techniques, including Generative AI, where they deliver clear business value Communicate technical concepts, progress, and impact effectively to business and technical audiences Compensation and Benefits: The expected compensation range for this position is between $120,300 - $205,150. Location, confirmed job-related skills, experience, and education will be considered in setting actual starting salary. Your recruiter can share more about the specific salary range during the hiring process. Bonus based on performance and eligibility target payout is 10% of annual salary paid out annually. Paid time off subject to eligibility, including paid parental leave, vacation, sick, and bereavement. In addition to salary, PepsiCo offers a comprehensive benefits package to support our employees and their families, subject to elections and eligibility: Medical, Dental, Vision, Disability, Health, and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan. Qualifications Master’s Degree in Data Science, Computer Science, Statistics, or equivalent 5+ years of experience in data science, with leadership or people-management responsibility Proven experience delivering machine learning solutions using diverse data sources Strong Python skills and ability to guide production-quality ML code Solid understanding of software engineering, data engineering, and MLOps best practices Experience with deep learning frameworks (TensorFlow or PyTorch) Strong foundation in statistical and mathematical modeling Experience with cloud-based and distributed computing platforms Experience with Generative AI, including LLMs, prompt engineering, and RAG architectures EEO Statement Our Company will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of the Fair Credit Reporting Act, and all other applicable laws, including but not limited to, San Francisco Police Code Sections 4901-4919, commonly referred to as the San Francisco Fair Chance Ordinance; and Chapter XVII, Article 9 of the Los Angeles Municipal Code, commonly referred to as the Fair Chance Initiative for Hiring Ordinance. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status. PepsiCo is an Equal Opportunity Employer: Female / Minority / Disability / Protected Veteran / Sexual Orientation / Gender Identity / Age. If you'd like more information about your EEO rights as an applicant under the law, please download the available EEO is the Law & EEO is the Law Supplement documents. View PepsiCo EEO Policy. Please view our Pay Transparency Statement

Responsibilities

Lead, mentor, and grow a team of data scientists, providing technical guidance and career development Own the end-to-end delivery of large-scale analytics and machine learning initiatives Define standards and best practices for model development, data pipelines, and ML production workflows Oversee the design, deployment, monitoring, and optimization of machine learning systems in production Partner with business stakeholders to translate strategic goals into data science roadmaps and measurable outcomes Drive adoption of modern techniques, including Generative AI, where they deliver clear business value Communicate technical concepts, progress, and impact effectively to business and technical audiences Compensation and Benefits: The expected compensation range for this position is between $120,300 - $205,150. Location, confirmed job-related skills, experience, and education will be considered in setting actual starting salary. Your recruiter can share more about the specific salary range during the hiring process. Bonus based on performance and eligibility target payout is 10% of annual salary paid out annually. Paid time off subject to eligibility, including paid parental leave, vacation, sick, and bereavement. In addition to salary, PepsiCo offers a comprehensive benefits package to support our employees and their families, subject to elections and eligibility: Medical, Dental, Vision, Disability, Health, and Dependent Care Reimbursement Accounts, Employee Assistance Program (EAP), Insurance (Accident, Group Legal, Life), Defined Contribution Retirement Plan.

Qualifications

Master’s Degree in Data Science, Computer Science, Statistics, or equivalent 5+ years of experience in data science, with leadership or people-management responsibility Proven experience delivering machine learning solutions using diverse data sources Strong Python skills and ability to guide production-quality ML code Solid understanding of software engineering, data engineering, and MLOps best practices Experience with deep learning frameworks (TensorFlow or PyTorch) Strong foundation in statistical and mathematical modeling Experience with cloud-based and distributed computing platforms Experience with Generative AI, including LLMs, prompt engineering, and RAG architectures