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Contract Causal Inference Jobs (NOW HIRING)

NewsBreak

Senior Recommendation Engineer

Mountain View, CA

$123K - $169K/yr

... contract so new, high-quality providers can win work. * Define evaluation frameworks that treat ... Publications or open-source contributions in recommendation, causal inference, or marketplace ...

New

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

Apply causal inference techniques (uplift modeling, difference-in-differences, synthetic controls ... Experience with B2B or ecommerce pricing, such as quote optimization, contract pricing, or price ...

Master Electronics

Data Scientist II

Phoenix, AZ · On-site

Apply causal inference techniques (uplift modeling, difference-in-differences, synthetic controls ... Experience with B2B or ecommerce pricing, such as quote optimization, contract pricing, or price ...

LatchBio

AI Biologist - Variant

San Francisco, CA · On-site +1

$120K - $180K/yr

... variant calling, GWAS, causal inference, and multiomics integration. Your benchmarks and ... Compensation & Logistics * 1099 (or W8-BEN) contract, 40 hrs/week, no end date * Fully performance ...

PhaseV

Medical Director

Cambridge, MA · On-site

Leveraging advanced causal inference and machine learning, PhaseV detects hidden signals in ... Support contract and scope-of-work discussions where clinical expertise is critical to positioning ...

Stash

Senior Data Scientist

New York, NY · Hybrid

Strong foundation in experimental design, causal inference, and applied machine learning ... contracts when measurement depends on them. * Communication: Excellent written and verbal ...

PhaseV

Medical Director

Cambridge, MA · On-site

Leveraging advanced causal inference and machine learning, PhaseV detects hidden signals in ... Support contract and scope-of-work discussions where clinical expertise is critical to positioning ...

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Contract Causal Inference information

What is a contract causal inference specialist?

A Contract Causal Inference specialist is a professional who applies statistical and analytical methods to determine cause-and-effect relationships within data, typically on a contractual or project basis. These specialists are often brought in to analyze business, healthcare, or social science data to help organizations make evidence-based decisions. They use techniques such as randomized controlled trials, regression analysis, and propensity score matching to isolate causal impacts. Contract roles are usually temporary and focused on specific projects or questions. This position requires strong statistical knowledge, programming skills, and the ability to communicate findings to non-technical stakeholders.

What are the key skills and qualifications needed to thrive as a contract causal inference specialist?

To thrive as a Contract Causal Inference Specialist, you need a strong background in statistics, econometrics, or data science, typically with an advanced degree in a quantitative field. Proficiency with statistical software like R, Python, and specialized causal inference packages, as well as experience with data wrangling tools, is essential. Exceptional analytical thinking, clear communication, and attention to detail are valuable soft skills for interpreting results and collaborating with clients. These competencies are vital for delivering robust, actionable insights that drive evidence-based decision-making in a contractual setting.

What are some common challenges faced by professionals in contract causal inference roles, and how can they be addressed?

Professionals in contract causal inference roles often encounter challenges such as working with incomplete or messy datasets, ensuring the validity of assumptions in causal models, and effectively communicating complex findings to stakeholders. Addressing these issues typically involves using robust statistical techniques, performing thorough data cleaning, and engaging in transparent documentation of the modeling process. Additionally, collaborating closely with subject matter experts and stakeholders can help clarify project goals and improve the relevance and impact of your analyses.

What is the difference between Contract Causal Inference vs Data Analyst?

AspectContract Causal InferenceData Analyst
Required CredentialsStatistics, Data Science, or related certifications; often advanced degreesBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch-focused, project-based, often in consulting or academiaBusiness environments, analyzing data to inform decisions
Employer & Industry UsageResearch institutions, consulting firms, tech companiesCorporations, marketing agencies, finance, healthcare
Search & Comparison IntentUnderstanding causal relationships, research projectsData analysis, reporting, business insights

Contract Causal Inference specialists focus on identifying cause-and-effect relationships through research and statistical methods, often in consulting or academic settings. Data Analysts interpret data to generate reports and insights for business decisions. While both roles require data skills, Contract Causal Inference emphasizes causal modeling and research, whereas Data Analysts focus on descriptive and diagnostic analysis.

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Contract Causal Inference jobs near you
Infographic showing various Contract Causal Inference job openings in the United States as of September 2026, with employment types broken down into 3% Internship, 81% Full Time, 15% Part Time, and 1% Contract. Highlights an 64% Physical, 3% Hybrid, and 33% Remote job distribution.

Senior Recommendation Engineer

Mountain View, CA

NewsBreak
See more jobs at NewsBreakexternal

$123K - $169K/yr

Full-time

Posted 3 days ago

New

Job description

About Nearby AI

Nearby AI is a new company from NewsBreak building a trust-first marketplace for local home services. We help homeowners understand a problem, what it should reasonably cost, and whether to hire at all before they are connected to a provider, and we help service providers win work on fit and outcomes rather than on speed of contact.

Our mission: give people clarity and control from the first sign of a problem to a job done right, and give good contractors work they are equipped to win.

We are a small founding team. We work from evidence, keep a written record of decisions, and hold a short set of product principles we do not trade for revenue: no pay-for-rank, no sharing of customer contact information beyond what the customer approved, and no quality claims we cannot substantiate.

The problem you will own

Most recommendation systems optimize for the next click. Ours has to support an infrequent, expensive, high-consequence household decision, where the right recommendation is sometimes to do nothing. You will build recommendation and matching across three surfaces: which content to surface to which user and when in a large content feed; an event-triggered engagement system that responds to changes in a user's situation; and two-sided matching between customers and service professionals under explicit fairness constraints. Ranking is never for sale, and the objective functions you design must reflect that.

Responsibilities
  • Build and own the recommendation and ranking systems for feed surfaces, from candidate generation through online experimentation and monitoring.
  • Design the event-triggered engagement engine with the messaging platform team: condition detection, propensity and uplift modeling, frequency management, and holdouts.
  • Build customer-to-professional matching, starting with transparent rules and evolving to learned ranking, with an explicit fairness and exposure contract so new, high-quality providers can win work.
  • Define evaluation frameworks that treat downstream outcomes and negative signals, including complaints and "do not proceed" recommendations, as first-class objectives.
  • Contribute learnings back to the company's pricing and intent models owned by the AI team.
Qualifications

Required

  • 5+ years of machine-learning or software engineering, including 3+ years shipping recommendation or ranking systems in production.
  • Built and operated a ranking system at consumer scale, millions of users, including candidate generation, ranking, and monitoring.
  • Substantial online experimentation experience on ranking changes, and can explain at least one test that failed and why.
  • Deployed an uplift, causal, or counterfactual model in production, or can give a rigorous account of why click-based objectives are wrong for rare, high-cost decisions.
  • Strong Python plus at least one of Java, Scala, or Go; production experience with feature stores and streaming pipelines.
  • Ability to explain modeling decisions to product and business stakeholders and to defend experimental design under commercial pressure.

Preferred

  • Feed or notification ranking at a content or media company.
  • Two-sided marketplace matching with fairness or exposure constraints in production.
  • Domains with low-frequency, high-consequence decisions such as insurance, healthcare, or real estate.
  • Publications or open-source contributions in recommendation, causal inference, or marketplace design.

Apply