Machine Learning Data Scientist - Consumer Forecasting
How well this resume fits
Compared with a sample resume. Compare your own
Not sure The job lists no required items.
How close the work is
Weak2 of 5 named skills are shown in described work and 0 are only listed or mentioned (rule: met = 1, partial = ½; strong ≥ 75%, moderate ≥ 50%).
How strong your resume shows it
WeakOf 5 supported items, 2 have strong evidence (a reported result or a confirmed answer) and 0 moderate (used in a described task).
What the job asks for
| What the job asks | Required? | Do you have it? | From your resume |
|---|---|---|---|
| Education degree Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research). | Not stated | Partly The résumé lists a degree below the level the job names. (Parsed from lower-quality page text; check it against the original posting.) | Lakeshore State University B.S. in Statistics, minor in Economics | 2016 – 2020 |
| Experience 7+ years of experience 7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems. | Not stated | Partly Dated Experience entries add up to 6.7 years; the job asks for 7+. Only the Experience section is counted, so check whether anything is missing. (Parsed from lower-quality page text; check it against the original posting.) | Data Analyst Junior Data Analyst Data Analytics Intern |
| Skill Python Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. | Not stated | Yes The résumé shows Python used with a reported result. (Parsed from lower-quality page text; check it against the original posting.) | Replaced a manual Excel carrier report with a scheduled Python and SQL job, saving the operations team about 8 hours a week. Wrote a demand forecast in Python (pandas, statsmodels) for 120 lanes that cut forecast error from 22% to 14%. Pulled and cleaned patient visit data (about 2M rows a month) in SQL and Python for clinic scheduling reports. |
| Skill SQL Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. | Not stated | Yes The résumé shows SQL used with a reported result. (Parsed from lower-quality page text; check it against the original posting.) | Replaced a manual Excel carrier report with a scheduled Python and SQL job, saving the operations team about 8 hours a week. Built and maintain 14 Tableau dashboards on SQL Server for on-time delivery, carrier cost and warehouse throughput, used by 60+ managers each week. Pulled and cleaned patient visit data (about 2M rows a month) in SQL and Python for clinic scheduling reports. |
| Skill scikit-learn Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. | Not stated | Not sure The résumé doesn't mention scikit-learn. That means unknown, not lacking. (Parsed from lower-quality page text; check it against the original posting.) | Nothing found |
| Skill PyTorch Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. | Not stated | Not sure The résumé doesn't mention PyTorch. That means unknown, not lacking. (Parsed from lower-quality page text; check it against the original posting.) | Nothing found |
| Experience building or scaling forecasting platforms in a high-growth company. | Not stated | Partly The résumé text names Forecasting, but the rules can't tell how it was used; check it. (Parsed from lower-quality page text; check it against the original posting.) | Wrote a demand forecast in Python (pandas, statsmodels) for 120 lanes that cut forecast error from 22% to 14%. |
| Familiarity with causal inference, Bayesian forecasting. | Not stated | Not sure The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.) | Nothing found |
| Experience building agentic decision support systems. | Not stated | Not sure The rules can't check this against the résumé. (Parsed from lower-quality page text; check it against the original posting.) | Nothing found |
| Skill machine learning Passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving environments. | Not stated | Not sure The résumé doesn't mention machine learning. That means unknown, not lacking. (Parsed from lower-quality page text; check it against the original posting.) | Nothing found |
How the match was worked out
Coverage: what the job asks, what the résumé shows, what is left
35% of the weighted requirements are covered by résumé evidence. preferred: met 0, partly 1, missing 2 · unknown: met 2, partly 2, missing 2
weight = 3 required / 2 preferred / 1 unknown, +1 emphasis (expert, strong, extensive…), +1 named in the job title, +½ per repeat (max +1); rating = 3 met, 2 partially met with the term in described work, 1.5 partially met (listed or mentioned), 0 unknown or mismatch; coverage = Σ weight×rating/3 ÷ Σ weight over rows that can be judged (work-authorization, location and clearance rows need your answer and are left out).
Missing from the résumé
- preferred r10 Passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving environments. (weight 5.0) missing: AI
- preferred r8 Familiarity with causal inference, Bayesian forecasting. (weight 2.0) missing: causal inference, Bayesian found: Forecasting
- unknown r5 Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. (weight 1.0)
- unknown r6 Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. (weight 1.0)
Only partly shown
- preferred r7 Experience building or scaling forecasting platforms in a high-growth company. (weight 2.0) found: Forecasting
- unknown r1 Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research). (weight 2.0) missing: Computer Science found: Statistics, Economics, Operations, Research
- unknown r2 7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems. (weight 2.0) missing: applied data science, Machine learning, marketplace systems found: Forecasting
Needs your answer
Nothing depends on an answer.
Sentences the rules cannot check (not counted)
- preferred r9 Experience building agentic decision support systems. (weight 2.0)
What the role does (not weighted)
On the résumé: Finance, Forecasting, Statistics, A/B testing, Research, Statistics, Economics, Operations, Python, SQL
Not on the résumé: Product, Engineering, Go, Data Science, Process improvement, Machine learning, Self-starter, Science, Manufacturing, KPIs, DAU, WAU, LTV, Stakeholder management, Collaboration, San Francisco, GTM, Design, LLMs / generative AI, Stakeholder management, Computer Science, applied data science, predictive modeling, marketplace systems, time-series forecasting techniques, explainability, scalability, tools, scikit-learn, PyTorch, TensorFlow, model monitoring, debugging, Communication, Presentation, Fast-paced environment
Job title terms: Machine learning (not found), Forecasting (found)
What the résumé says (as the rules read it)
Skills: Python (supported by outcome), SQL (supported by outcome), Tableau (supported by outcome), Excel (supported by outcome), pandas (supported by outcome), PostgreSQL (used in task), A/B testing (listed), Git (listed), Matplotlib (listed), NumPy (listed), Power BI (listed)
Roles: Data Analyst at Harborline Logistics (Jun 2022 – Present); Junior Data Analyst at Cedar Ridge Health Network (Jul 2020 – May 2022); Data Analytics Intern at Lakeshore State University, Office of Institutional Research (May 2019 – Aug 2019)
Years in dated roles: 6.7
Education: B.S. in Statistics (completed)
Certifications: Tableau Desktop Specialist, Google Data Analytics Professional Certificate
Quantified achievements: 4
On the résumé but not asked by this job: Healthcare, Reporting, Dashboards, SQL querying, Supply chain, Data warehousing, Data cleaning, Active Directory, Data analysis, Spreadsheets
Résumé terms the rules don't know: OH, Server, Network, Office, Access, NYC, Taxi Trip Analysis, Tableau Public, Harborline, Economics, Professional Certificate (matched by plain text only; tell us if one should be a skill)
Job description
About the Team The Strategic Finance team at OpenAI plays a critical role in shaping the company’s long-term trajectory. We partner closely with Product, Engineering, and Go-To-Market teams to inform high-stakes decisions through rigorous data science and economic modeling. As part of our expanding Data Science function, we’re building a best-in-class Forecasting capability to drive real-time, data-driven decision-making across user growth, revenue, compute infrastructure, and more. We are developing scalable forecasting infrastructure to help us understand and anticipate business dynamics in an increasingly complex, usage-based world. Our models are foundational to planning, pricing, operational efficiency, and growth strategy - supporting key investment decisions and unlocking OpenAI’s full potential. About the Role We’re looking for a senior Machine Learning Data Scientist to lead our forecasting initiatives. You’ll be one of the founding members of the Forecasting pillar within Strategic Finance Data Science, responsible for building and scaling robust, interpretable, and production-ready forecasting systems. Your models will power critical business decisions by predicting core metrics such as DAU/WAU, revenue, LTV, compute consumption, and profitability. This is a highly cross-functional role, requiring technical excellence, strong product intuition, and business acumen. You’ll collaborate with product managers, researchers, engineers, and finance leaders to operationalize forecasting insights, influence company-wide strategy, and build foundational forecasting capabilities at OpenAI. 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: - Build statistical and machine learning models to solve forecasting needs across product, finance, infrastructure, and GTM domains. - Own the end-to-end modeling lifecycle, including scoping, feature engineering, model development and prototyping, experimentation, deployment, monitoring, and explainability. - Develop and productionize scalable, interpretable forecasts for user growth, monetization, compute load, customer lifetime value, and profitability. - Contribute to self-service forecasting tools and internal platforms, enabling teams across OpenAI to access and act on real-time predictions. - Research and evaluate emerging tools and techniques in the forecasting space, such as TimeGPT, large language model extensions, causal forecasting, and hybrid approaches. - Drive strategic insight generation by translating technical outputs into business-aligned recommendations and decision frameworks. - Collaborate closely with cross-functional teams to ensure forecasts are well-integrated into planning processes, experimentation workflows, and executive decision-making. You might thrive in this role if you have: - Advanced degree (MS or PhD) in a quantitative field (e.g., Statistics, Computer Science, Economics, Operations Research). - 7+ years of experience in applied data science, with deep hands-on exposure to forecasting, predictive modeling, or marketplace systems. - Expertise in time-series forecasting techniques and practical understanding of model trade-offs across performance, explainability, and scalability. - Proficiency in Python, SQL, and tools such as scikit-learn, PyTorch/TensorFlow, and forecasting libraries. - Demonstrated experience with model monitoring, debugging, and long-term maintenance in production environments. - Strong communication and storytelling skills - able to simplify complexity and influence executive stakeholders. - Self-directed, intellectually curious, and comfortable leading ambiguous projects from 0→1. Bonus if you have: - Experience building or scaling forecasting platforms in a high-growth company. - Familiarity with causal inference, Bayesian forecasting. - Experience building agentic decision support systems. - Passion for AI and a strong point of view on how machine learning should inform strategic decisions in fast-moving environments. 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, and requests can be made via this link. 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.