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Phd Causal Inference Jobs in Ohio (NOW HIRING)

Strong grounding in causal inference and experimental design, including the ability to distinguish ... MS or PhD in Statistics, Computer Science, Operations Research, or a related quantitative field.

... causal analyses) * Fine-tune, prompt-engineer, and evaluate large language models for domain ... Master\'s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field (or ...

Phd Causal Inference information

What are the key skills and qualifications needed to thrive as a PhD Causal Inference researcher, and why are they important?

To thrive as a PhD Causal Inference researcher, you need advanced knowledge of statistics, econometrics, and causal modeling, typically supported by a doctoral degree in a quantitative field. Familiarity with statistical programming languages (such as R or Python), specialized software (like STATA or SAS), and experience with experimental or quasi-experimental methods are essential. Strong analytical thinking, attention to detail, and the ability to communicate complex findings clearly make a candidate stand out. These skills ensure rigorous, credible research that can inform policy, product development, or scientific understanding by accurately identifying causal relationships.

What collaborative opportunities can a PhD specializing in Causal Inference expect within a multidisciplinary research team?

PhD professionals in Causal Inference frequently collaborate with experts from fields such as epidemiology, economics, computer science, and public health. They often work closely with data scientists, subject matter experts, and statisticians to design studies, interpret complex datasets, and develop robust analytical models. This multidisciplinary environment fosters continuous learning and often leads to co-authorship on research publications, participation in grant writing, and involvement in high-impact policy or product decisions. Effective communication and teamwork skills are essential to translate technical findings for diverse audiences and drive actionable insights.

What is a PhD in Causal Inference?

A PhD in Causal Inference is an advanced research degree focused on understanding and identifying cause-and-effect relationships using statistical and computational methods. Students in this field learn to design studies, analyze data, and develop new methodologies to answer complex causal questions in areas such as social sciences, medicine, economics, and artificial intelligence. Graduates often work in academia, research institutions, or industries where evidence-based decision-making is essential.
What cities in Ohio are hiring for Phd Causal Inference jobs? Cities in Ohio with the most Phd Causal Inference job openings:
Staff Data Scientist- Pricing Science

Staff Data Scientist- Pricing Science

CSC Generation

Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 21 days ago


Job description

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.

Reports to: Director of Finance and Business Intelligence

Location: Remote — US or Canada

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About the RoleAs our Staff Data Scientist, you will design and ship production pricing systems such as demand forecasting, price elasticity modeling, dynamic pricing and the experimentation infrastructure needed to measure whether they actually work.   This is a hard, high-stakes problem: your models will directly influence margin and revenue decisions across a portfolio of brands operating at scale. You will own the full arc from framing ambiguous business problems as well-defined ML tasks through to monitoring models that hold up in production.   At six months, success looks like at least one pricing model shipped to production with measurable business impact and an experimentation framework in place that your stakeholders trust. If you have spent time building pricing systems from the ground up, not just consuming them, and you care deeply about rigorous causal inference and honest model evaluation, this role was written for you.


What You\'ll Do
  • Design and build production ML systems for pricing, demand forecasting, and related revenue problems
  • Frame ambiguous business problems as well-defined ML tasks with clear success criteria and measurable outcomes
  • Set the standard for model evaluation, validation, and monitoring — including knowing when CV metrics are misleading and when holdout testing is the only honest answer
  • Build robust predictive models across classification, regression, time series, and causal inference
  • Identify and prevent data leakage, overfitting, and other failure modes before they reach production
  • Design and analyze experiments to measure causal impact of pricing decisions
  • Debug models that fail in production — understand why they fail, not just that they do
  • Translate model limitations, uncertainty, and risk clearly to both technical and non-technical stakeholders
  • Partner with product, engineering, and business teams to ensure ML solutions solve real problems


Required Qualifications
  • 7+ years of applied ML / data science experience with a track record of production systems that delivered measurable business impact.
  • Deep experience in pricing, demand forecasting, or revenue optimization — you have built these models end-to-end, not just consumed them.
  • Expert-level Python and SQL.
  • Deep understanding of ML fundamentals beyond API-level usage, including model evaluation, validation, and failure mode diagnosis.
  • Strong grounding in causal inference and experimental design, including the ability to distinguish correlation from causal result.
  • Ability to work with messy, real-world data and make pragmatic tradeoffs under ambiguity.
  • Familiarity with cloud ML platforms (GCP/Vertex AI or AWS/SageMaker).
  • MS or PhD in Statistics, Computer Science, Operations Research, or a related quantitative field.


Preferred Qualifications
  • Experience in e-commerce, retail, marketplace, or pricing-intensive industries such as airlines, ride-sharing, or fintech.
  • Demonstrated ability to operate autonomously on ambiguous problems without requiring fully scoped requirements.


Why JoinThe people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
  • Portfolio-Level Impact: Your models will influence pricing and margin decisions across a $1B+ portfolio of brands — the output of your work is visible at the executive level from day one.
  • AI-First Skill Building: Get hands-on with production ML infrastructure, causal inference at scale, and the Genesis platform — building a modern, applied ML skill set on real retail data problems.
  • Ownership Without Bureaucracy: You will own the full problem from framing through production, with the autonomy to make technical decisions and the stakeholder access to see them through.
  • Competitive Benefits (CAN): Comprehensive benefits including paid time off, RRSP match, group benefits, and employee discounts across portfolio brands.
  • Competitive Benefits (US): Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.


Interview Process Step 1 — Recruiter Intro: 30-minute call to cover background, role fit, and logistics. Step 2 — Hiring Manager Interview: Conversation with the Chief Administrative Officer focused on leadership philosophy and AI-native HR experience. Step 3 — Technical / Case Discussion: Deep dive into a people ops problem or system design scenario with cross-functional stakeholders. Step 4 — Executive Interview: Final conversation(s) with senior leadership. Step 5 — Offer: Reference checks conducted in parallel where possible.


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For US-based candidates, this posting is intended for candidates that reside in the following states:

AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY.

For Ontario applicants, please note that this posting is for an existing vacancy.

The CSC Generation family of brands provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, provincial, state or local laws. 

The CSC Generation family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact hrbenefits@cscshared.com.


CSC Generation logo

About CSC Generation

Sourced by ZipRecruiter

CSC Generation is a multi-brand technology platform based in Merrillville, IN, United States. The organization operates in the retail sector and utilizes technology to save retail companies from going into bankruptcy, while also offering consumers the ability to lease their purchases. Founded by serial entrepreneur, Justin Yoshimura, CSC Generation has leveraged its proprietary technology and customer database to quickly revitalize distressed retail brands. The company's mission revolves around the concepts of reinvention and innovation as it aims to redefine traditional retail and direct-to-consumer models in today's digital age. Notably, the company has, to date, acquired several brands such as DirectBuy, Killion, and most notably, Z Gallerie, growing fast within the e-commerce sector.

Company size

501 - 1,000 Employees

Headquarters location

Merrillville, IN, US

Year founded

2016