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associate, digital finance transformation - data scientist - private equity performance improvement (open to all us locations) Jobs in San Francisco United States

data scientist business

OpenAI

About the Team
 
The Business Data Science team uses data and analytics to optimize business performance, drive growth, and foster meaningful partnerships, with the goal of ensuring the sustained and impactful expansion of OpenAI's initiatives to maximize the benefits of AGI for all of humanity. We partner with Sales (GTM), Marketing, Partnerships, Support, Finance, Product, and Growth.
 
About the Role
 
As a member of our Business Data Science team, you will help build a data-driven culture around insight generation, decision making, and strategy at OpenAI. This role is focused on driving customer success within our business products (ChatGPT Team, ChatGPT Enterprise, and API). You will work on projects such as identifying opportunities for interventions within a customer lifecycle to drive activation & onboarding, identifying target audiences for new feature launches, and measuring the efficacy of emails, events, and other interventions to drive ongoing engagement with our products.
 
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
 
In this role, you will: 
Embed with our Customer Success organization as a trusted partner, uncovering new ways to drive customer adoption and engagement of our business products.
 
Establish key metrics, run experiments, and perform analysis to help us understand the incrementality of our efforts to drive adoption/engagement.
 
Proactively surface insights and opportunities to drive engagement and growth.
 
Build tools and systems for stakeholders to self-serve routine data and insights freeing up time to work on more leveraged analyses.
 
Become an expert in OpenAI’s data and systems. Through partnership with Data Eng, Finance and other business teams, you will self-serve all the underlying data for our business and derive insights from them.
 
Partner with other data scientists across the company to share knowledge and continually synthesizing learnings across the organization
 
You might thrive in this role if you have: 
At least 7+ years of experience in Data Science roles within dynamic, outcome-driven organizations.
 
Expertise in statistics and causal inference, applied in both experimentation and observational causal inference studies.
 
Proficiency in quantitative programming languages, such as Python and R.
 
Expertise in SQL, with extensive experience extracting large datasets and designing ETL workflows.
 
Experience using business intelligence tools, such as Mode, Tableau, and Looker.
 
Strategic and impact-driven mindset, capable of translating complex business problems into actionable frameworks.
 
Ability to build relationships with diverse stakeholders and cultivate strong partnerships.
 
Strong communication skills, including the ability to bridge technical and non-technical stakeholders and collaborate across various functions to ensure business impact.
 
Ability to craft clear data stories using decks, memos, and dashboards to drive decision-making at every level.
 
Best-in-class attention to detail and unwavering commitment to accuracy.
 
Proven track record in solving problems within Finance, Marketing, Partnerships, Sales, Support, or other GTM areas.
 
About OpenAI
 
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
 
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
 
For additional information, please see
 
OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement
 
.
 
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
 
To notify OpenAI that you believe this job posting is non-compliant, please submit a report through
 
this form
 
. No response will be provided to inquiries unrelated to job posting compliance.
 
We are committed to providing reasonable accommodations to applicants with disabilities.
 
OpenAI Global Applicant Privacy Policy
 
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
 
Compensation
 
$255K – $405K + Offers Equity

Data Scientist, Business

Uber

<div>About the Role
 
 
Have you ever wondered why it's taking so long for an earner to be matched to your trip, why the ETA is so long, or how an Earner is picked from the many around you? If so, the Mobility Matching Science team is for you!
 
 
The Matching team at Uber builds the systems that determine the optimal way to fulfill trips on the Mobility platform. We work on the problems of determining which earners to send an offer to and when. The solutions we build are critical for maintaining reliability and ensuring the trust of riders and earners alike.
 
 
We are looking for experienced scientists who relish the opportunity to develop novel approaches and apply them at Uber's scale. They ideally have a good balance of causal inference, analysis, experimentation, and modeling knowledge, as well as, an ability to use these skills to identify business opportunities and deliver product recommendations.
 
 
What You'll Do
<ul><li>Develop data-driven business insights and work with cross-functional stakeholders to identify opportunities and recommend prioritization of product, growth and optimization initiatives
</li><li>Design and analyze experiments, communicating results that draw detailed and actionable conclusions
</li><li>Analyze and contribute to development of optimization algos and ML models for use in mobility matching
</li><li>Collaborate with cross-functional teams such as product, engineering and operations to drive system development end-to-end from conceptualization to final product
</li></ul>Basic Qualifications
<ul><li>Ph.D. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
</li><li>Minimum 2 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
</li><li>Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
</li><li>Proven experience in experimental design (e.g., A/B testing) and causal inference.
</li><li>Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
</li><li>Experience with exploratory data analysis, statistical analysis and testing, and model development.
</li></ul>Preferred Qualifications
<ul><li>Ph.D., or M.S. in Statistics, Economics, Machine Learning, Operations Research, Computer Science, or another quantitative field.
</li><li>Minimum 5 years of industry experience as an Applied Scientist, Data Scientist, or in a similar quantitative role.
</li><li>Strong knowledge of the mathematical foundations of statistics, machine learning, optimization, and economics.
</li><li>Proven experience in experimental design (e.g., A/B testing) and causal inference.
</li><li>Proficiency in using Python or R for data analysis, modeling, and algorithm prototyping at scale with large datasets.
</li>Experience with exploratory data analysis, statistical analysis and testing, and model development. 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Sr. Scientist - Mobility Matching

Data Scientist, Product Analytics

Owner.com

<div>About Owner.com
Owner is the AI growth system for local restaurants. 
Our AI continuously improves SEO, marketing, and online ordering to grow first-party orders. Unlike other companies that force small business owners to master their software to drive sales, Owner gives them a proven system run by experts. 
Owner is like having an army of engineers and marketers on your side, just like the big chains. 
Our vision
We&rsquo;re starting by helping independent restaurants succeed online. 
But it&rsquo;s not just restaurants that need our help. Most local businesses are struggling with these same problems. Huge technology corporations are taking their customers, bleeding their profits, and making it hard for them to survive. 
Once we nail the solution for restaurants &ndash; we&rsquo;ll scale it into every other local business type. 
In the future we envision, tens of millions of local business owners will use our technology to succeed in the digital age. 
Read our Series C memo here 
Our traction
Since 2020, we've generated tens of millions in revenue and processed over half a billion dollars of online orders. 1 in 5 Americans have used an Owner.com website. 
More importantly, we&rsquo;ve helped over 20,000 restaurant owners, and saved them nearly $200 million in fees. 
Our team
Our team is now in the low hundreds. We&rsquo;ve got top talent from the most successful companies in SMB software, including: Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire and Stripe. 
We&rsquo;ll be scaling even faster in 2026 to keep pace with our customer growth. 
Where we work
Owner is a remote-first, global company headquartered in San Francisco, with a sales hub in Toronto. For a few of our roles we prioritize in-person collaboration at one of our office locations. Most of our teammates are distributed throughout the globe. Please review the role description and discuss with your recruiter for more details on location. 
Why we&rsquo;re looking for you
We&rsquo;re building an effective, impactful Product Analytics function at Owner.com. As a Risk &amp; Payments Data Scientist, you will play a pivotal role in shaping the product roadmap through close collaboration with Product Managers to establish proper metrics and impact sizing. You will drive experimentation initiatives and manage the testing framework. You will be instrumental in designing and implementing KPIs for our payments product squad, identifying gaps and potential opportunities that will help grow the business. The best candidates will not just provide insights to the EPD (Engineering &amp; Product) org, but be a strategic driver, identifying gaps/opportunities in the money &amp; risk area, building internal alignment around them, and creating meaning business impact.
We&rsquo;re migrating from Stripe Standard to Custom Connect / Adyen for Platforms, which means we&rsquo;ll own far more of the risk surface: merchant onboarding/underwriting, fraud detection, dispute mitigation, reserves, and payout risk. As a Risk Data Scientist you will be building the models, signals, and decisioning that protect our merchants and our P&amp;L&mdash;while keeping conversion high and friction low. 
For this particular role we are focused on candidates located in the San Francisco Bay Area. We are a remote-first company with a home base in SF, where our team comes together for periodic in-person collaboration (most local teammates opt to come in on Tuesdays/Thursdays). For more details chat with your recruiter!
The impact you will have<ul><li>
Payments modeling:</li><li>
Cut losses without killing conversion: Ship ML models that reduce fraud/chargebacks and credit losses while maintaining checkout auth rates and onboarding pass-through.</li><li>
Accelerate safe growth: Create merchant risk scores and dynamic controls (e.g., reserves/holdbacks, payout delays) that scale to 10k+ restaurants.</li><li>
Give Ops superpowers: Build signals, alerts, and tools that let our Payments Ops / Risk Ops team review what matters&mdash;and automate the rest.</li><li>
Make risk measurable: Define loss budgets and risk SLIs/SLOs; deliver dashboards that make risk tradeoffs explicit.</li><li>
Build and maintain ML models for merchant underwriting, transaction fraud, chargeback propensity, payout risk.</li><li>
Design reusable frameworks for feature generation, model training, deployment, and monitoring so we can add new models quickly without reinventing the wheel.</li><li>
Payments analytics:</li><li>
Own analytics for payments, billing, and risk features, from user checkout experience to internal financial reporting.</li><li>
Monitor and improve critical KPIs such as payment success rate, failed payment recovery, fraud rates, chargeback volume, and revenue leakage. Set up monitoring for drift, stability, and business KPIs, with automated alerts.</li><li>
Identify and size revenue &amp; risk opportunities across the payments funnel (from checkout to Stripe to invoice collection).</li><li>
Partner with Product Managers on AB tests and experiments related to payments UX, fraud flags, or risk workflows.</li><li>
Collaborate with Engineering to instrument new product features and ensure great event tracking and data integrity in payment flows.</li><li>
Enhance product planning influencing product planning through informative impact sizing, enabling more strategic decision-making.</li><li>
Improve data Integrity and quality: Collaborate with developers on database design to strengthen data integrity and quality.</li><li>
Establish a Single Source of Truth (SSOT): Work alongside Data &amp; Analytics Engineers to implement robust models in DBT and Snowflake, and design dashboards that provide a unified view of business-critical data. Integrate third-party and processor signals (Stripe Radar, Adyen RevenueProtect, device/identity data) into our models.</li></ul>
Who you&rsquo;ll work with<ul><li>
Reporting Structure: This role reports directly to our VP, Business Operations, Jonathan Shenkman.</li><li>
Technical Collaboration: You will collaborate with Analytics Engineers on all technical aspects, including data modeling, data quality, and the use of tools like DBT and Snowflake.</li><li>
Work hand-in-glove with Payments Ops &amp; Risk Lead to encode policy into models, define review queues, and reduce manual workload.</li><li>
Collaborate with Payments PM/GM on onboarding UX, step-up flows, and dispute tooling; quantify conversion vs. loss tradeoffs with clear, dollarized impact.</li><li>
Provide merchant-level insights (watchlists, risk cohorts) and playbooks (what to hold, what to terminate, what to educate).</li></ul>
What we're looking for<ul><li>
4&ndash;8+ years in applied ML or risk data science (fintech/payments, marketplace, or anti-fraud).</li><li>
Hands-on with Python, SQL, and ML libraries/frameworks; comfortable with MLflow (or equivalent), and feature stores.</li><li>
Proven track record shipping production models that materially reduced losses or improved conversion; strong offline evaluation + online experimentation skills.</li><li>
Strong understanding of MLops and experience deploying MLops systems</li><li>
Experience deploying machine learning models to an api for realtime predictions</li><li>
Experience building systems for model observability after deployment</li><li>
Deep familiarity with payments/risk concepts: KYC/KYB, underwriting, auth vs capture, chargebacks, friendly fraud, card testing, reserves, payout returns, soft/hard declines.</li><li>
Strength in feature engineering on messy, imbalanced data; rigorous cost-sensitive evaluation (ROC/PR, cost curves, business impact).</li><li>
Excellent communicator who can turn model output into clear decisions and dollarized tradeoffs.</li><li>
Strong grasp of metrics design, experimentation, and product funnel analysis.</li><li>
Ability to handle ambiguity, deep dive into financial systems, and proactively flag problems before they escalate.</li><li>
KPI Development: A proven track record of developing KPIs and metrics tailored for product squads, particularly within a startup environment.</li><li>
Data Product Expertise: Ability to build comprehensive end-to-end data products.</li><li>
Organizational Skills: Highly organized with a keen eye for precision.</li><li>
Industry Experience: Prior experience in SaaS and/or startup environments is highly preferred. Experience in the restaurant industry is an added advantage.</li></ul>
Pay and benefits<ul><li>
The estimated base salary range for this role is $200k - $240k, plus a generous equity pre-IPO equity package</li><li>
Other benefits include comprehensive health coverage, unlimited PTO - plus extra fun perks!</li></ul>
Notice - Employment Scams
Communication from our team regarding job opportunities will only be made by an Owner team member with an @owner.com email address.
We do not conduct interviews over email or chat platforms, and we will never ask you to provide personal or financial information such as your mailing address, social security number, credit card numbers or banking information. If you believe you are being contacted by scammer, please mark the communication as &quot;phishing&quot; or &ldquo;spam&rdquo; and do not respond.</div>

Staff Product Data Scientist, Risk and Payments

<div>We're looking for PhD candidates to work with the Competitive Intelligence Team as a Scientist intern during summer 2026 (12 weeks). As an intern, you will be embedded in a product team working on solving real-world Uber problems under the supervision of an analyst on that team, and will have the opportunity to partner closely with Scientists, Software Engineers, Product Managers, and other cross-functional partners.
 
 
About the Role
 
 
The Competitive Intelligence team works to understand the strategy and performance of Uber and other companies with similar products through a combination of external and internal data. Uber operates in a diverse array of markets and product categories filled with rapidly evolving competing options; assessing the success or failure of a product or strategy depends on understanding this context. Doing this, and doing it well, is an interesting and challenging problem. The team uses its data analysis, statistical modeling, economics, and business expertise to build scalable data products and ship actionable insights.
 
 
We are looking for candidates passionate about solving new and difficult problems with data. You will build out and own competitive metrics/models, provide key insights, conduct deep dive analysis to understand new opportunities and empower teams to make more informed business and strategy decisions with competitive intelligence.
 
 
What You'll Do
<ul><li>Work with a mentor closely to define a business problem, scope a project, develop, and prototype the solution using data-driven approaches
</li><li>Work with engineers and product managers to turn prototypes into scalable solutions
</li><li>Present findings to leaders to inform decisions
</li><li>Establish standard methodologies for science such as modeling, coding, analytics, optimization, and experimentation
</li></ul>Basic Qualifications
<ul><li>Pursuing a Ph.D. majoring in Statistics, Machine Learning, Economics, or other related quantitative fields
</li><li>Candidates should have at least one semester/quarter left of their education after finishing the internship
</li><li>Strong problem-solving and analytical abilities
</li></ul>Preferred Qualifications
<ul><li>Coding proficiency in areas such as R, Spark, SQL, Python
</li><li>Background in data visualization via open-source libraries/packages or third-party tools (i.e. Tableau, Mixpanel, Looker, or similar)
</li><li>Knowledge of underlying mathematical foundations of statistics, machine learning, optimization, stochastic processes, economics, and analytics
</li><li>Experience in the following areas: Exploratory Data Analysis, Statistical Analysis, ML Model Development, Competitive Intelligence, Causal Inference, Operations Management, Transportation
</li><li>0-2 years of prior work experience in an analytical setting
</li><li>Organized, detail-oriented and able to work independently on multiple projects at once
</li><li>Ability to communicate effectively with both technical and business partners
</li><li>Open to feedback, excellent at implementing newly learned ideas and concepts
</li><li>Research mentality with a bias towards action to structure a project from idea to prototype to implementation
</li>Independence, self-starter mindset, excellent communication, and outstanding follow-through - you energetically tackle your work and love the responsibility of being individually empowered 
<li>
</li></ul> 
 
For San Francisco, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For Seattle, WA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For Sunnyvale, CA-based roles: The base hourly rate amount for this role is USD$67.00 per hour. For all US locations, you will also be eligible for various benefits.</div>

2026 PhD Scientist Intern (Competitive Intelligence)

Decagon

<div>
 
 About Decagon
 
 
 
 Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.
 
 
 Our technology enables industry-defining enterprises like Avis Budget Group, Block&rsquo;s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.
 
 
 We&rsquo;re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We&rsquo;re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.
 
 
 We&rsquo;re an in-office company, driven by a shared commitment to excellence and velocity. Our values &mdash; Just Get It Done, Invent What Customers Want, Winner&rsquo;s Mindset, and The Polymath Principle &mdash; shape how we work and grow as a team.
 
 
 
 About The Role
 
 
 
 As a Data Scientist at Decagon, you&rsquo;ll bring deep technical rigor to how data is analyzed, tested, and applied across the business. You&rsquo;ll design and interpret experiments, apply advanced statistical methods, and build scalable analytical frameworks that directly inform product strategy and company performance.
 
 
 You&rsquo;ll work across Engineering and Product to establish robust measurement practices, expand the use of data-driven decision making, and uncover insights that shape Decagon&rsquo;s trajectory. This role blends strong analytical expertise with product acumen and the ability to influence at the highest levels.
 
 
 
 In this role, you will
 
 
 
 <ul>
 <li>
 Build and maintain foundational datasets and metrics that reflect infrastructure usage, system performance, and scaling efficiency across Decagon's AI platform.
 </li>
 <li>
 Enhance and extend Decagon&rsquo;s data models, metrics, and dashboards to ensure consistent, scalable reporting across teams.
 </li>
 <li>
 Partner closely with Engineering to shape strategy through data &mdash; surfacing bottlenecks, inefficiencies, and scaling risks before they become problems.
 </li>
 <li>
 Collaborate with Product and Engineering to embed experimentation and measurement best practices into product development cycles.
 </li>
 <li>
 Build and continuously improve Decagon&rsquo;s data stack, ensuring scalable models, pipelines, and tools that empower self-serve analytics across the company.
 </li>
 <li>
 Communicate findings clearly and persuasively to executives and cross-functional stakeholders, influencing strategic decision-making.
 </li>
 <li>
 Conduct deep-dive quantitative investigations to support incident analysis, reliability improvements, and post-mortem learnings.
 </li>
 <li>
 Evaluate and interpret the performance of ML models (e.g., classification, recommendation, LLM-based systems), helping the team reason through trade-offs, limitations, and business implications of model-driven decisions.
 </li>
 <li>
 Be the primary driver of experimentation culture at Decagon, helping define how we design, measure, and scale experiments across the business.
 
 
 </li>
 </ul>
 
 What We're Looking For
 
 
 
 <ul>
 <li>
 Strong ability to structure ambiguous problems, translate them into analytical approaches, and deliver business impact through data.
 </li>
 <li>
 Advanced proficiency in SQL and Python, with a solid foundation in statistics and experimental design.
 </li>
 <li>
 5+ years (or equivalent experience) in data science or product analytics roles, ideally within high-scale consumer or marketplace products.
 </li>
 <li>
 Ability to reason about and analyze machine learning models, understanding their assumptions, trade-offs, and impact on product and customer outcomes.
 </li>
 <li>
 Proven track record as a data partner in large-scale backend or infrastructure systems.
 </li>
 <li>
 Comfort building operational analytics (e.g., alerting, SLIs/SLOs) and partnering closely with data and infra engineering.
 </li>
 <li>
 Excellent communication skills, with experience influencing stakeholders and driving decisions across teams.
 
 
 </li>
 </ul>
 
 Benefits
 
 
 
 <ul>
 <li>
 Medical, dental, and vision benefits
 </li>
 <li>
 Take what you need vacation policy
 </li>
 <li>
 Daily lunches, dinners and snacks in the office to keep you at your best
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 $250k - $300k + equity
</div>

Senior Data Scientist

<div>About the Role
 
 
Scientists at Uber use data to improve and automate all aspects of Uber's core rideshare and delivery products. You will be joining the Delivery team, which owns all aspects of our delivery experience - from eaters, couriers to merchants!
 
 
We are looking for experienced candidates with a passion for solving new and difficult problems with data. In this role, you will be able to utilize your strong quantitative skills to improve the Uber delivery experience as well as the overall marketplace performance.
 
 
About the Team (We are hiring for multiple teams)
 
 
We are building the most preferred online delivery platform globally and driving innovation across a number of areas including:
<ul><li>Fulfillment: Delivery is the core of our product. We carefully refine a living and breathing marketplace to balance long run outcomes with the everyday experiences of our customers and couriers.
</li><li>Consumer: We help users all over the world find what they need on UberEats. To do so we build state of the art recommendation systems and novel user experiences to help users discover the selection around them.
</li><li>Ads: Ads is one of the fastest growing lines of businesses at Uber. We work on end-to-end experience across lines of business, from enrolling advertisers, to designing auction and bidding, and integrating ads into the user experience.
</li><li>Offer: We optimize merchant offers, by identifying the optimal selection to make the platform affordable to consumers, by innovating on pricing models to align incentives, and by maximizing return for merchants through targeting.
</li><li>Merchant: Dedicated to optimizing merchant experience and driving merchant success at every stage of their lifecycle. We drive strategic and product decisions that expand merchant selections and maximize the value merchants bring to our marketplace.
</li></ul>What You'll Do
<ul><li>Solve ambiguous, challenging business problems using data-driven approaches, including ML, Optimization, and Causal Inference.
</li><li>Develop and implement statistical/econometric methodologies to improve results validity, power and generalizability.
</li><li>Develop data-driven business insights and work with cross-functional customers to find opportunities and recommend prioritization of product, growth, and optimization initiatives.
</li><li>Design and analyze experiments, present results that provide actionable recommendations.
</li><li>Orient the teams around data-driven product development by driving the creation of logging, metrics, data visualization and diagnostic tools, and experimentation paradigms.
</li><li>Define how our teams measure success, by developing metrics, in close partnership with cross-functional partners.
</li><li>Owning the product development cycle end-to-end from data and science aspects.
</li></ul>Basic Qualifications
<ul><li>Ph.D., M.S., or Bachelors degree in Statistics, Economics, Operations Research, or other quantitative fields.
</li><li>Minimum 4 years of industry experience as an Applied or Data Scientist or equivalent (2+ years if holding a Ph.D.)
</li><li>Experience in experimental design and analysis, exploratory data analysis and statistical analysis.
</li><li>Ability to use Python/R to work efficiently at scale with large data sets.
</li></ul>Preferred Qualifications
<ul><li>Experience managing projects across large, ambiguous scopes and driving initiatives in a fast moving, cross-functional environment.
</li><li>Expertise in synthesizing complex technical analyses into clear insights to influence product direction.
</li><li>Excellent communication skills across technical, non-technical, and executive audiences.
</li>Experience within a Delivery or Ecommerce business. 
<li>
</li></ul> 
 
For New York, NY-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.</div>

Senior Scientist, Delivery (Multiple Teams)

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