About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Financial Services.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

---

Team Description

Join an elite Applied AI team within AI Foundations at a Confidential Client. The team operates at the intersection of deep research and large-scale production systems to deliver personalized customer experiences across web and mobile. You’ll help design and deploy recommendation engines powered by research in foundation models, reinforcement learning, causal inference, transformer architectures, and recommender systems — all at scale across billions of interactions.

---

What You’ll Do

You will work cross-functionally to build and ship high-impact personalization and recommendation solutions.

• Partner with data scientists, software engineers, and product managers to deliver customer-facing products
• Design, train, evaluate, validate, and deploy machine learning models for personalization and recommendation
• Work with large-scale numeric and textual datasets using tools such as Python, Conda, AWS, H2O, and Spark
• Translate technical results into clear business recommendations and communicate trade-offs to stakeholders
• Drive experiments and iterate on models to improve customer outcomes and system robustness

---

Basic Qualifications

Candidates must meet one of the following education + experience paths:

• Bachelor’s degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or related) plus 2 years of experience performing data analytics
• Master’s degree in a quantitative field or an MBA with a quantitative concentration

Additional required skills/experience:

• Practical experience developing data science solutions with open-source tools
• Ability to work with large, heterogeneous datasets and perform data engineering to prepare features
• Strong communication skills to present technical work to business partners

---

Preferred Qualifications

• Master’s or PhD in a STEM field (Science, Technology, Engineering, or Mathematics)
• Experience working with AWS and cloud-based ML platforms
• 2+ years experience in Python, Scala, or R
• 2+ years experience applying machine learning in production
• 2+ years experience with SQL and big-data tooling (Spark, H2O, etc.)
• Familiarity with recommender systems, transformer architectures, causal inference, or reinforcement learning

---

Salary & Compensation

For candidates working in San Jose, CA, the full-time annual salary range for this role is:

• $148,000 - $168,900 (annual)

Actual pay offered will depend on the candidate’s experience, location, and other job-related factors. This role is also eligible for performance-based incentives (cash bonus and/or long-term incentives) per the client’s compensation plans.

---

Workplace, Sponsorship & Compliance

• This is a full-time position.
• Confidential Client will consider sponsoring a qualified applicant for employment authorization for this position.
• The client provides a comprehensive benefits package. Eligibility and specifics vary by role, location, and employment status.
• This role follows applicable US federal, state, and local employment laws and hiring practices.

---

Why Apply

• Work on state-of-the-art personalization and recommendation problems at production scale
• Join a research-forward team that moves ideas into customer impact
• Collaborate with talented cross-functional partners and influence product direction

---

Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.