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About This Role
About Ingredion:
Join Ingredion, where innovation impacts lives worldwide! Without even realizing it, you’ve experienced our work in your favorite chocolate, your pet’s food, the paper you write on, and your everyday snacks. As a global powerhouse across more than 60 industries, we harness the potential of natural ingredients to transform lives. Whether you’re just starting your career or bringing years of experience, there's a place for you here to make a real difference. Be part of our team that values a wide range of perspectives and is committed to shaping a better world with every product we create.
Location: Bridgewater, NJ or Westchester, IL
Workplace type: On\-Site/Hybrid (3 days/week on\-site \& 2 day/week remote)
As a Principal Data Scientist, you will be our data science guru, our technical trailblazer, and the mastermind behind a scalable, adaptive data lakehouse and analytical layer that transforms raw data into strategic insight. Your work will empower our global formulators and food scientists to unlock the full potential of over a century of collective knowledge, guiding experimentation, formulation, and product development with precision and purpose. You will accelerate innovations \- bringing the potential of people, nature and technology together to make life better.
We are looking for a trusted advisor on technical strategy, a mentor to your peers, and a champion of impactful data science. If you’re ready to build something extraordinary, apply now and help us shape the future of predictive formulation.
The Principal Data Scientist reports to the Director, Innovation – Digital
What you will do:
You’ll be at the forefront of AI/ML advancements applying cutting\-edge techniques to real\-world challenges and driving measurable business outcomes.
- Leading the design and implementation of a robust data framework that evolves with our innovation business.
- Structuring and scaling a dynamic data lakehouse and democratic analytical layer in Google Cloud, which supports our vision for using data to guide ingredient selection, predictive formulation strategies, and customer\-centric innovation.
- Collaborating with technical resources to stand up scalable machine learning pipelines that can quickly adapt to new challenges.
- Developing and deploying machine learning models tailored to evolving business needs,
- Mentoring the next generation of data scientists \- fostering a culture of excellence.
- Supporting the Director – Innovation, Digital to select \& drive use cases that will accelerate our ability use data to hone in on the right solutions for our customers.
What you will bring :
- Years of work experience in predictive modeling, data science and analysis – where you have demonstrated the ability to set the long term technical vision and see it through (with a strong preference for those who have done this within a Google ecosystem).
- Proven delivery of multiple major data science initiatives providing measurable value and insight to business stakeholders \- articulating and translating business questions, using statistical techniques to arrive at a solution using available data.
- Technical leadership – from your degrees in relevant quantitative fields, on top of the ability to utilize data scripting languages (e.g. SQL, Python) and understand statistical methods and advanced modeling techniques (e.g. machine learning, Bayesian inference).
- Exceptional leadership, stakeholder management, and collaboration skills, with a proven ability to inspire and speak about technical concepts to business, technical and lay audiences.
- Preferably a strong background in Food or Global ingredient solutions – to understand the challenges and opportunities of new solution formulation using data.
Who you are :
- Visionary \- able to step out of the day to day and think strategically about the future of insights in our space – you won’t just be thinking about today’s challenges – but what comes next.
- Curious and eager to evolve \- continuously exploring emerging technologies, including generative AI, ML platforms, and advanced modeling techniques.
- A mentor – who values taking the time to bring up all those around you – supporting their technical growth.
- Collaborative – with a demonstrated ability in stakeholder management and the ability to collaborate across a diverse group of stakeholders to achieve outcomes.
- Results oriented – with the ability to identify and focus on value when tackling multiple opportunities, helping all around you to work towards measurable outcomes/KPI.
Why Join Ingredion?
Discover why Ingredion is the ideal place to advance your career with our exceptional rewards and benefits package designed to help you thrive. Create the future with us and enjoy:
- Total Rewards Package – Competitive salary and performance\-based pay recognizing your contributions to our success
- Comprehensive Benefits \& Wellness Support – Health, long\-term savings, and resources for your physical, mental, and emotional well\-being
- Career Growth – Learning, training, and development opportunities, including tuition reimbursement
- Employee Recognition Program – A culture of real\-time appreciation, with personalized recognition rewards globally
- Employee Discount Program – Provides exclusive discounts on everyday products, services, and travel
\#LI\-JG1
We are an equal opportunity employer and value diversity at our company. Ingredion seeks to provide a work environment that is free from harassment and discrimination. We will not tolerate any form of discrimination based on race, color, religion, age, gender, gender identity, gender expression, national origin, ancestry, handicap or disability—mental or physical—marital status, sexual orientation, veteran status, disability resulting from military service, or any other classification protected by law (“protected classifications”). We are committed to establishing and maintaining a work environment where everyone is treated with dignity and respect.
Ingredion provides accommodations to job applicants with disabilities throughout the hiring process. If a job applicant requires an accommodation during the application process or through the selection process, we will work with the applicant to meet the job applicant's accommodation needs.
Ingredion uses AI\-enabled tools to support parts of the recruitment process, including resume screening and interview scheduling. These tools help match candidate skills to job requirements and streamline communication. All AI\-assisted decisions are reviewed by our Talent Acquisition team to ensure fairness and compliance with applicable laws. By applying, you acknowledge that AI may be used to support your application journey.
Relocation Available:
Yes, Within CountryPay Range:
$123,500\.00\-$164,700\.00 Annual
This pay range is not a guarantee of compensation or salary. Final base salary will be determined based on several factors which may include but are not limited to responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data and applicable bargaining agreement (if any).
Incentive Compensation:
As a part of the total compensation package, this role may be eligible for the Ingredion Annual Incentive Plan or a role\-specific commission/bonus.
Benefits:
Full\-time roles are eligible for our comprehensive benefits package which includes medical, dental and vision coverage as well as a 401(k) plan with an competitive company match.
Salary Context
This $123K-$164K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Ingredion, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($144K) sits 27% below the category median. Disclosed range: $123K to $164K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Ingredion AI Hiring
Ingredion has 1 open AI role right now. They're hiring across Data Scientist. Based in Bridgewater, NJ, US. Compensation range: $164K - $164K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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