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Data Science Phd Jobs in Oregon (NOW HIRING)

Qualifications: * 5-8+ years in data science, applied ML, or statistics, shipping production models ... MS/PhD preferred. * Ability to leverage generative AI to increase output quality and speed.

OR · On-site

$79K - $108K/yr

This clinical leadership role shapes the data validation pipeline for our cell-free DNA (cfDNA ... Advanced degree in Life Sciences (MD, PhD, PharmD, MS, RN, or equivalent scientific or clinical ...

Graduate degree (Masters or PhD) in operations research, applied mathematics, control systems ... data science.

OR · On-site

$466K - $750K/yr

... PhD or Master's) in Computer Science, Statistics, Mathematics, or related quantitative field. Proficiency in Python, Scala or Java. Deep knowledge of machine learning, optimization, and data analysis ...

Showing results 21-40

Data Science Phd information

What are the key skills and qualifications needed to thrive as a data science PhD?

To thrive as a Data Science PhD, you need advanced expertise in statistics, machine learning, data analysis, and a doctoral degree in a quantitative field. Proficiency in programming languages like Python or R, experience with big data frameworks (e.g., Spark, Hadoop), and familiarity with data visualization tools are typically required. Critical thinking, problem-solving, and strong communication skills help you translate complex data insights for diverse stakeholders. These skills are vital for driving innovative research, making data-driven decisions, and contributing impactful solutions in data-centric environments.

What are some common challenges faced by data science PhDs when transitioning from academia to industry roles?

Data Science PhDs often encounter challenges such as adapting to the faster pace and collaborative nature of industry projects compared to academic research. In industry, there is a greater emphasis on delivering practical solutions within tight deadlines and working closely with cross-functional teams like engineering and product management. Additionally, data science work in industry may require balancing technical rigor with business impact, often prioritizing actionable insights over exhaustive analysis. Building strong communication and stakeholder management skills can help ease this transition.

What is a data science PhD?

A Data Science PhD is a doctoral-level degree focused on advanced research in data science, which combines elements of statistics, computer science, and domain expertise. Students in a Data Science PhD program typically work on developing new methods for analyzing large datasets, creating machine learning algorithms, and addressing complex problems in areas such as artificial intelligence, data mining, and predictive analytics. Graduates are prepared for careers in academia, research, and industry, where they can lead data-driven projects and contribute to advancements in the field.
What are popular job titles related to Data Science Phd jobs in Oregon? For Data Science Phd jobs in Oregon, the most frequently searched job titles are:
What cities in Oregon are hiring for Data Science Phd jobs? Cities in Oregon with the most Data Science Phd job openings:
Infographic showing various Data Science Phd job openings in Oregon as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior Data Scientist, Retention & Product

CookUnity

OR

Full-time

Medical, Vision, Retirement, PTO

Re-posted 16 days ago


Job description

The role:

We're hiring an ML-focused Data Scientist to own retention and churn for CookUnity. In a weekly subscription business, retention is the engine of growth - the gap between a healthy customer and a churned one is often just a few weeks of activity. Your job is to predict who's at risk, understand why they leave, and power the interventions that keep customers ordering and win back the ones who go. This is a hands-on role embedded with Product, CRM, Marketing, and Engineering.

Responsibilities:
  • Churn & retention modeling: Build churn/survival models and lifecycle-state models that flag at-risk customers early and anticipate where each customer is heading.
  • Intervention & reason understanding: Power the retention interventions that act on that risk - save flows, skip/pause deflection, lifecycle messaging - and classify why customers churn so the response fits the reason rather than being generic.
  • Resurrection & win-back: Develop propensity models and personalized experiences that bring churned customers back.
  • Personalization & Next-Best-Action: Decide the right action, offer, and message per customer across product surfaces, with an uplift layer measuring incremental impact.
  • Offer & promo optimization: Determine who gets an incentive, when, and at what value - maximizing retained revenue without over-discounting.
  • Production & MLOps: Own the full model lifecycle and ship models into the systems where they act; handle monitoring, retraining, and drift.
  • Experimentation & incrementality: Design experiments and uplift measurement so we intervene on movable customers and not on those who'd retain anyway.

Qualifications:
  • 5-8+ years in data science, applied ML, or statistics, shipping production models.
  • Retention/churn depth: churn prediction, survival / time-to-event modeling, and lifecycle-state models in a subscription or recurring-revenue context.
  • Causal & experimentation rigor: uplift/incrementality measurement and A/B testing, with the judgment to separate true impact from selection effects.
  • End-to-end ML & MLOps: building, validating, and deploying models in production (CI/CD, registries, containerization, orchestration, monitoring).
  • Engineering & tooling: strong Python (pandas, scikit-learn, gradient boosting; deep learning a plus), SQL, code hygiene and reproducibility.
  • Collaboration: excellent communication; able to embed with Product/CRM/Marketing and turn models into decisions.
  • Education: BS in a quantitative field required; MS/PhD preferred.
  • Ability to leverage generative AI to increase output quality and speed.
Preferred requirements:
  • Subscription marketplaces, food-tech, or consumer marketplaces with a retention mandate.
  • Lifecycle-state / Hidden Markov models for churn.
  • Causal and uplift libraries (e.g. EconML) or survival-modeling packages.
  • Recommenders, embeddings, or personalization for retention and win-back.

Learn More About CookUnity

We believe great leadership starts with alignment on vision, values, and ways of working. To give you deeper insight into who we are and what we're looking for, we invite you to explore: CookUnity's Leadership Principles - The values and behaviors that guide how we operate, collaborate, and scale.

We hope this provides valuable insight into our culture and product vision. If this excites you, we'd love to connect!


Benefits

  Health Insurance coverage

 401k Plan

 Unlimited PTO

 5- year Sabbatical: After 5 years with CookUnity, you get a 4-week paid sabbatical

 Paid Family leave

 Compassionate Leave: 3-5 days each time the need arises

 A generous amount of CookUnity credits to enjoy our amazing meals, added to your account, monthly

AI-forward workplace: enterprise access to ChatGPT and Claude to help you work smarter and grow faster.

 Wellness perks: access to fitness subsidies to build a healthy lifestyle

 Personalized Spanish coach

 Awesome opportunity to join a company that is looking to change how we eat and how chefs work!