We are looking for a Senior Machine Learning Scientist to join our AI Group in Berlin. As a Senior Machine Learning Scientist, you will be responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers' hands. You will work in partnership with Product and Design functions of teams we support. Our team's dedicated ML product engineers enable us to move to production fast, often shipping to beta in weeks after a successful offline test. We are passionate about applying machine learning technology, and have productized everything from classic supervised models, to cutting-edge unsupervised clustering algorithms, to novel applications of transformer neural networks. We test and measure the real customer impact of each model we deploy.
\nYour responsibilities will include identifying areas where ML can create value for our customers, identifying the right ML framing of product problems, working with teammates and Product and Design stakeholders, conducting exploratory data analysis and research, deeply understanding the problem area, researching and identifying the right algorithms and tools, being pragmatic, but innovating right to the cutting-edge when needed, performing offline evaluation to gather evidence an algorithm will work, working with engineers to bring prototypes to production, planning, measuring & socializing learnings to inform iteration, and partnering deeply with the rest of team, and others, to build excellent ML products.
\nTo be successful in this role, you will need to have broad applied machine learning knowledge, 3-5 years applied ML experience, practical stats knowledge (experiment design, dealing with confounding etc), intermediate programming skills, strong communication skills, both within engineering teams and across disciplines, comfort with ambiguity, typically have advanced education in ML or related field (e.g. MSc), and scientific thinking skills. Bonus skills and attributes include track record shipping ML products, PhD or other experience in a research environment, deep experience in an applicable ML area. e.g. NLP, Deep learning, Bayesian methods, Reinforcement learning, clustering, strong stats or math background, visualization, data skills, SQL, matplotlib, etc.
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\nYour day-to-day will involve owning analytical coverage across growth marketing, registration, and website, setting measurement frameworks, building scalable reporting, and driving the analytical roadmap for your domain. You will lead analysis on complex, ambiguous questions: incrementality testing, LTV and payback modeling, attribution deep dives, funnel decomposition, and cohort analysis.
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\nWe are looking for someone with 4–8 years of experience in a data or analytics role, with hands-on experience in at least one marketing channel: paid search (preferred), paid social, TV/CTV, affiliates, direct mail, or similar. You should understand how that channel's data works, how it's structured, what the key metrics are, and how performance is measured and reported. Multi-channel experience is a strong plus.
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\nYou should write readable, well-structured Python for analytical work: data manipulation, automation, or scripting. Your code should be something a teammate can pick up and maintain. You should understand how attribution models work, what makes a good experiment, and how to think about LTV, CAC, and payback period without needing a primer.
\nYou should be proactive and not wait to be asked. You should spot the gap, scope the work, and drive it to a conclusion. Your stakeholders shouldn't have to manage your backlog. You should be able to take a fuzzy business question and turn it into a clear analytical plan. You should ask the right clarifying questions and bring structure without needing the problem handed to you pre-scoped.
\nYou should communicate findings in a way that drives decisions. You should know when a chart is enough and when you need a narrative. You've presented to senior stakeholders and know how to calibrate.
\nPreferred experience includes breadth across multiple marketing channels: paid search, paid social, TV/CTV, affiliates, or others. Hands-on experience with experiment design and incrementality testing is also preferred. Experience with Snowflake or a similar cloud data warehouse is a plus. Experience mentoring or reviewing the work of junior analysts is also a plus.
\nWork perks at Greenlight include medical, dental, vision, and HSA match, paid life insurance, AD&D, and disability benefits, traditional 401k with company match, unlimited PTO, paid company holidays and pop-up bonus holidays, professional development stipends, mental health resources, 1:1 financial planners, fertility healthcare, 100% paid parental and caregiving leave, plus cleaning service and meals during your leave, flexible WFH, both remote and in-office opportunities, fully stocked kitchen, catered lunches, and occasional in-office happy hours, employee resource groups.
\nOur stance on salaries is that Greenlight provides a competitive compensation package with a market-based approach to pay and will vary depending on your location, experience, and skill set. The total compensation package for this position will also include a discretionary performance bonus, equity rewards, medical benefits, 401K match, and more. Greenlight conducts continuous compensation evaluations across departments and geographies to ensure we are keeping our pay current and competitive.
\nThe estimated base pay range for this position in NY, CA, WA is $125,000-145,000. The estimated base pay range for this position in CO is $125,000-135,000.
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\nThe Personalization team, within the Machine Learning Chapter and Engineering Department, plays a central role in implementing algorithms that utilize personalization signals to optimize for business KPIs like revenue & conversions.
\nYou will work closely with engineers and product managers to define success, uncover insights, and influence roadmap decisions through data. Your work will help shape innovative user experiences that drive business KPIs, interpret user behavior, and drive product and algorithm improvements.
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\nManage multi-million dollar media budgets with a clear focus on CAC efficiency and return on ad spend
\nTake a full funnel performance approach from impression to activated paying user — including landing pages, onboarding flow touchpoints, and trial conversion
\nDrive continuous optimization and a rigorous testing culture
\nIdentify performance gaps and inefficiencies across existing campaigns and channels; develop and execute a prioritized roadmap to address them
\nDesign and run structured A/B and multivariate experiments across creative, copy, audiences, landing pages, and bidding strategies
\nEstablish benchmarks, performance baselines, and a regular cadence of insights reporting to stakeholders
\nScale what's working — and build what's missing
\nLead channel expansion efforts: identify emerging acquisition opportunities in programmatic, connected TV, or new affiliate partnerships
\nPartner with Creative and Design teams to develop and iterate on high-performing ad assets optimized for each channel and audience
\nDevelop audience segmentation strategies that target the right users — with channel-appropriate messaging
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\nPartner with Data to ensure accurate measurement of paid channel contribution across the self-serve funnel
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\nTrack and report on full-funnel metrics
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\n7+ years of performance marketing experience with expertise and hands-on expertise specifically in SEM/Paid Search. Experience with other channels such as paid social (Meta, TikTok, LinkedIn), display, programmatic, and affiliate channels are valuable
\nProven track record inheriting and meaningfully improving an existing paid acquisition program — not just maintaining, but finding new efficiency and growth levers
\nDeep fluency in performance measurement: you understand attribution models, incrementality, MMM basics, and can design experiments that produce actionable signal
\nStrong analytical skills — you're comfortable pulling your own data, building dashboards, and translating numbers into decisions without waiting for a data team
\nExperience marketing consumer or prosumer SaaS products with a product-led or freemium growth model at a high growth tech company
\nCreative instincts backed by data — you know how to brief creative teams and evaluate assets against performance goals, not just aesthetics
\nBenefits
\nCompetitive Salary & Equity
\n401(k) Program with a 4% match
\nHealth, Dental, Vision and Life Insurance
\nShort Term and Long Term Disability
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\nCompensation
\nThe base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
\nMore details about our benefits are available to candidates during the hiring process.
\nThis role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.
\nAbout the Team
\nOpenAI's Training team is responsible for producing the large language models that power our research, our products, and ultimately bring us closer to AGI. Achieving this goal requires combining deep research into improving our current architecture, datasets and optimization techniques, alongside long-term bets aimed at improving the efficiency and capability of future generations of models. We are responsible for integrating these techniques and producing model artifacts used by the rest of the company, and ensuring that these models are world-class in every respect. Recent examples of artifacts with major contributions from our team include GPT4-Turbo, GPT-4o and o1-mini.
\nAbout the Role
\nAs a member of the architecture team, you will push the frontier of architecture development for OpenAI's flagship models, enhancing intelligence, efficiency, and adding new capabilities.
\nIdeal candidates have a deep understanding of LLM architectures, a sophisticated understanding of model inference, and a hands-on empirical approach. A good fit for this role will be equally happy coming up with a creative breakthrough, investing in strengthening a baseline, designing an eval, debugging a thorny regression, or tracking down a bottleneck.
\nThis role is based in San Francisco. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
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\nAbout OpenAI
\nOpenAI 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.
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\nLocation
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\nDepartment
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\nCompensation
\nThe base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance related bonus for eligible employees and benefits.
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\nThis role is at-will and OpenAI reserves the right to modify base pay and other compensation components at any time based on individual performance, team or company results, or market conditions.
\nAbout the Team
\nCodex is OpenAI’s first-party developer product focused on agentic software engineering. We’re building tools that help engineers design, write, test, and ship code faster—safely and at scale. We partner tightly with research and product to translate model advances into tangible developer productivity.
\nAbout the Role
\nAs a Data Scientist on Codex, you will measure and accelerate product-market fit for AI developer tools. You’ll define what “developer productivity” means for our product, run experiments on new coding models and UX, and pinpoint where the model helps or hurts across languages and tasks. Your insights will directly shape how an entire industry builds software.
\nThis 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.
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\nLocation
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\nEmployment Type
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\nDepartment
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\nThe base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.
\nBenefits
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\nThe ChatGPT team operates at the intersection of research, engineering, product, and design to bring OpenAI’s technology to a global audience.
\nWithin ChatGPT, the Growth Partnerships team is dedicated to expanding distribution, unlocking new user acquisition channels, and building high-leverage integrations that deliver ChatGPT to users where they are. Working closely with external partners and internal platform teams, we design product experiences, APIs, and growth surfaces that scale adoption while upholding trust and safety.
\nOur work is multidisciplinary—merging product, engineering, and business impact to transform partnerships into sustainable growth engines for ChatGPT.
\nAbout the Role
\nWe are seeking an experienced Full Stack Engineer to join the ChatGPT Growth Partnerships team and help establish the technical foundation that underpins partner-led growth. You will work on end-to-end product experiences—including frontend applications, backend services, APIs, experimentation, and data—that facilitate seamless integrations, onboarding, activation, and monetization through partners.
\nThis high-impact role is ideal for engineers who thrive in fast-paced, ambiguous settings, can move from concept to launch, make sound product and technical judgments, and deliver quickly while maintaining robust engineering standards.
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\nJoin us to help grow the ChatGPT partner ecosystem and reach millions of users through thoughtful engineering and product leadership.
\nAbout OpenAI
\nOpenAI 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.
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\nLocation
\nSan Francisco; Seattle
\nEmployment Type
\nFull time
\nDepartment
\nData Science
\nCompensation
\nThe base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance related bonus for eligible employees and benefits.
\nBenefits
\nAbout the Team
\nOur Applied team brings OpenAI technologies to consumers and businesses around the world. We collaborate across research, engineering, design and business functions to turn cutting-edge AI advancements into impactful real-world applications. Our team has been behind notable product launches (ChatGPT, API, Sora), creating tools that help developers write code, enable businesses to operate more efficiently, and empower individuals to learn and create. As AI capabilities rapidly evolve, we focus on ensuring that our products are safe, accessible, and beneficial to all.
\nAbout the Role
\nAs a Data Scientist on the Applied Product team, you will contribute to a data-driven product development culture for consumer and enterprise products at OpenAI. This is critical as our products reach millions of users and businesses worldwide. We are focused on aligning both research and product development to drive measurable impact for these individuals and organizations alike.
\nYou should expect to define our north-star metrics, design A/B tests, and establish source-of-truth dashboards that the entire company can use to answer their own product questions. Most importantly, you should expect to be a core member of the product development team.
\nThis role is based in San Francisco, CA or Seattle, WA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
\nIn this role, you will:
\nYou might thrive in this role if you have:
\nYou could be an especially great fit if you have:
\nAbout OpenAI
\nOpenAI 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.
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\nSan Mateo, CA, United States Data Science & Analytics ID: 5750
\nEvery day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.
\nAt Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device.
\nA career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone.
\nThe Data Science & Analytics organization's mission is to increase our speed, frequency and acumen of making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum including analytical data engineering, product analytics, causal inference, economics, statistical modeling and machine learning. Aligned and partnering with product verticals, we use this extensive toolbelt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products and measure impact on our community of players and developers.
\nThis is a temporary, part-time position requiring no more than 20 hours per week _for a 3-month duration, with possibility to extend._
\nTeams Hiring for this role:
\nYou Will:
\nYou Have:
\nYou may redact age, date of birth, and dates of attendance/graduation from your resume if you prefer.
\nFor roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual hourly rate could fall outside of this expected range. This pay range is subject to change and may be modified in the future. _Please note that not all benefits shown on this page are applicable to internship opportunities._
\nHourly Pay Range
\n$64—$64 USD
\nRoles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).
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\nWhat you'll do
\nYou'll find and diagnose quality issues in our search pipeline, design metrics from scratch to track and measure search quality, build datasets for model training, including LLM-as-a-judge labeling pipelines, improve search snippet quality and page selection algorithms for indexing, and design and analyze A/B experiments to validate improvements.
\nWhat you need
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