research scientist

Achira

<div><h1 class="jobSectionHeader"><b>Why Achira</b></h1><p>
At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed.</p><p>
At Achira, you&rsquo;ll operate at the frontier scale of massive compute, massive data, and massive ambition. You&rsquo;ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We&rsquo;re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success.</p><p></p><h1 class="jobSectionHeader"><b>
About the Role</b></h1><p>
We&rsquo;re looking for machine learning researchers who want to shape the frontier of generative models for the atomistic microcosm. You will work at the intersection of cutting-edge machine learning, statistical mechanics, and approximate bayesian inference to help us conquer sampling problems at light-speed. In addition, you&rsquo;ll collaborate with experts in chemistry and physics to invent and implement models to unlock what&rsquo;s possible for Achira&rsquo;s microscopic world models.</p><p>
While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities.</p><p></p><h1 class="jobSectionHeader"><b>
What You&rsquo;ll Do</b></h1><ul><li><p>
Invent advanced sampling and simulation methods that integrate probabilistic inference, deep learning, and reinforcement learning to enable efficient exploration and simulation of learned energy landscapes for molecular systems.</p></li><li><p>
Design and train frontier generative models: diffusion, autoregressive, flow-based, and latent-variable architectures.</p></li><li><p>
Build models that can map between data distributions to bridge the gap between simulation and reality.</p></li><li><p>
Prototype, benchmark, and iterate rapidly to transform research ideas into reusable and scalable components across Achira&rsquo;s ecosystem.</p></li><li><p>
Collaborate with physicists and chemists to ensure models are grounded in real physics.</p></li><li><p>
Work with research engineers and the infrastructure team to identify where research ideas will need support in order to deliver effective results.</p></li></ul><p></p><h1 class="jobSectionHeader"><b>
About You</b></h1><ul><li><p>
Interested in building generative models that describe real matter.</p></li><li><p>
Drive to build at the frontier of what&rsquo;s possible and try out new, high-risk ideas.</p></li><li><p>
Machine learning researcher with professional experience (post-degree) in an industry setting.</p></li><li><p>
Demonstrated research impact through conference talks or publications (in machine learning venues), open-source contributions, or released models.</p></li><li><p>
Strong interdisciplinary communication and presentation skills and the ability to translate ideas and concepts to colleagues from non-ML backgrounds.</p></li><li><p>
Proficiency in Python and modern ML frameworks (PyTorch, JAX).</p></li><li><p>
Experience collaborating on research projects across multi-person teams.</p></li></ul><p></p><h1 class="jobSectionHeader"><b>
Nice to Have</b></h1><p>
Achira values excellent ML researchers from many backgrounds, and expect members of the team to contribute complementary strengths. If the work excites you, we encourage you to apply, even if you hit none of the bonus features listed below!</p><ul><li><p>
Experience working with models that operate on 3-D point clouds and dynamic data.</p></li><li><p>
Experience in sequential monte carlo methods.</p></li><li><p>
Experience with probabilistic programming.</p></li><li><p>
Experience with pre-training, mid-training, and post-training (especially reenforcement learning) parts of the model development process.</p></li><li><p>
Familiarity with statistical mechanics: working knowledge of sampling, estimators, and the Crooks/Jarzynski perspective of nonequilibrium statistical mechanics.</p></li><li><p>
Prior experience working in or with researchers in the domains of computational chemistry, biology, or materials science.</p></li><li><p>
Experience working with multi-cloud distributed compute systems.</p></li><li><p>
Experience working with multi-site distributed company team.</p></li></ul><p>
Compensation Range: $164,638 - $259,000</p></div>

ML Research Scientist (MLRS) - Generative AI

Genentech

<div><div><h3 class="jobSectionHeader"><b>The Position</b></h3>
<p>NOTE: Qualified candidates for both South San Francisco, CA and Mississauga, Canada will be considered equally.</p>
<p></p><p>At Roche, we thrive to deliver more benefits to our patients as part of our 10-year Pharma vision. An integral part of achieving this vision is to deliver new and innovative data analytics solutions to our scientists across Pharma Technical Operations (PT). PT Digital and Operational Excellence (PTE) is the organization that catalyzes the global development and execution of PT&rsquo;s Digital and Operational Excellence strategy to enable PT to realize our performance promises. We build a strong cross-functional and inclusive community, put the power of data into the hands of our people, further develop the Lean and Digital skills across PT, and scale up our Digital and Advanced Analytics solutions, for the benefit of our colleagues and patients.</p>
<p></p><p>We aim to activate data citizenship and digital mind, revolutionize our FAIR data ecosystem and systems landscape, re-design excellent processes, and generate transformative insights. We collaborate closely with global functions to deliver impactful value to our patients.</p>
<p></p><h3 class="jobSectionHeader"><b>The Opportunity</b></h3>
<p></p><p>As a Senior Data Scientist and Statistician, you will play a critical role in advancing data science and statistical solutions across PT, with a special focus on <b>biologics development</b>. You will collaborate with scientists, engineers, business leaders, and fellow data scientists to design experiments, analyse data, and develop tools that generate insights, improve efficiency, and accelerate drug development.</p>
<p>Beyond hands-on analytics, you will mentor junior colleagues, promote best practices, and lead complex projects - shaping the way data science and statistics contribute to PT&rsquo;s success.</p>
<p></p><p><b>Main Responsibilities</b></p>
<ul><li><p>Provide statistical expertise and develop data science solutions to Pharma technical operations, including process development, analytical method development, commercial manufacturing and quality control for biologics.</p>
</li><li><p>Partner with cross-functional teams to frame critical scientific and operational questions, translating business needs into analytical strategies.</p>
</li><li><p>Design and oversee studies to generate high-quality data for robust statistical analysis.</p>
</li><li><p>Apply advanced statistical methods, machine learning, and AI to interrogate complex datasets, driving actionable insights.</p>
</li><li><p>Interpret results and clearly communicate findings, influencing strategic and operational decisions.</p>
</li><li><p>Develop and maintain software tools, algorithms, and automated workflows for data integration, cleaning, and large-scale analysis across heterogeneous sources.</p>
</li><li><p>Build and sustain strong collaborations with internal and external stakeholders.</p>
</li><li><p>Design and deliver training in statistics and data science to upskill the organisation.</p>
</li><li><p>Stay current with emerging methods, regulatory guidance, and industry trends in statistics, data science, and drug development.</p>
</li><li><p>Mentor junior colleagues, guiding them in study design, statistical analysis, coding best practices, and modern data science approaches, building a collaborative and supportive learning environment.</p>
</li></ul><p></p><h3 class="jobSectionHeader"><b>Who you are</b></h3>
<p></p><ul><li><p><b>Educational Qualification:</b> Master's or Ph.D. in Statistics, Biostatistics, data science, and relevant fields.</p>
</li><li><p><b>Expertise and Experience:</b> Extensive experience in designing experiments (DOE), analyzing complex datasets, combined with a deep understanding of pharmaceutical technical development, particularly biologics. Advanced proficiency in statistical methods and software (e.g., R, JMP), with a proven ability to lead DOE for process characterisation, optimisation, and validation.</p>
</li><li><p><b>Regulatory and Collaborative Skills: </b>Strong familiarity with regulatory guidelines (FDA, EMA), excellent collaborative skills for working in multidisciplinary teams, and outstanding communication skills for reporting and stakeholder liaison.</p>
</li><li><p><b>Problem-Solving and Project Management: </b>Strong analytical and problem-solving skills, ability to manage multiple projects, and high attention to detail to ensure accuracy and reliability.</p>
</li><li><p><b>Continuous Improvement and Learning: </b>Self-motivated, able to work independently and coaching junior colleagues in teams, keen on staying updated with new developments in statistics and data science, and passionate about continuous improvement and learning.</p>
</li></ul><p></p><p>Relocation benefits are not available for this posting.</p>
<p></p><p>The expected salary range for this position based on the primary location of South San Francisco, CA, is $156,200 to $290,200. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.</p>
<p><b>Benefits</b></p>
<p></p><p>#LI-PK1</p>
<p></p><p>Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.</p>
<p></p><p>If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.</p>
</div><p></p><div><h2 class="jobSectionHeader"><b>JOB FACTS</b></h2>
<div><div><ul><li><h3 class="jobSectionHeader"><b>Job Sub Category</b></h3>
<p>Computational Biology</p>
</li><li><h3 class="jobSectionHeader"><b>Schedule</b></h3>
<p>Full time</p>
</li><li><h3 class="jobSectionHeader"><b>Job Type</b></h3>
<p>Regular</p>
</li><li><h3 class="jobSectionHeader"><b>Posted Date</b></h3>
<p>Jun 30th 2026</p>
</li><li><h3 class="jobSectionHeader"><b>Job ID</b></h3>
<p>202606-116612</p></li></ul></div></div></div></div><p></p>

Senior Data Scientist

Greptile

<div>
 We want to build agents that autonomously validate code changes. Today that looks like AI that reviews pull requests in GitHub, catching bugs and enforcing standards. We&rsquo;re reviewing close to 1B lines of code a month now for over 3,000 companies.
 <br/>
 <br/>
 <strong>
  Problems We&rsquo;re Excited About
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Coding standards can be idiosyncratic and are often poorly documented; can we build agents that learn them through osmosis like a new hire might?
  </li>
  <li>
   Can we identify for each customer what types of PR feedback they do and don&rsquo;t care about, perhaps using some sample efficient RL, in order to increase signal-to-noise ratio?
  </li>
  <li>
   Some bugs are best caught by running the code, potentially against discerning AI-generated E2E tests. Can we autonomously deploy feature branches and use agents to parallel try to break the application to detect bugs?
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Trajectory
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   7,000+ customers
  </li>
  <li>
   Raised $30M from Benchmark, YC, Paul Graham, Initialized
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Team
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   We have assembled a small, talent dense team who have scaled critical functions at companies like Stripe, Google, Figma, LinkedIn, etc.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   BS in Computer Science or equivalent
  </li>
  <li>
   Some research experience, ideally with ML/LMs/Agents
  </li>
  <li>
   Strong programming skills and good product intuition
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Responsibilities
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experiment with and apply recent advancements in agents/LMs etc. to improve the performance and capabilities of our products
  </li>
  <li>
   Example: you might study multi-agent architectures, prototype and evaluate a multi-agent code review workflow, and then work with a team to integrate successful prototypes into production systems
  </li>
  <li>
   Stay current with the latest research in LLMs, information retrieval, and developer tooling
   <br/>
   <br/>
  </li>
 </ul>
 Compensation Range: $180K - $280K
 <br/>
 <br/>
</div>


Research Engineer

Common Sense Media

<div><p><b>Data Scientist</b></p><div></div><div><div><div><div><p><b>
Organization</b></p><p><br>
Common Sense Media</p></div><div><p><b><br>
Department</b></p><p><br>
Data</p></div><div><p><b><br>
Reports To</b></p><p><br>
Senior Director, Data &amp; Analytics</p></div><div><p><b><br>
Location</b></p><p><br>
San Francisco, CA</p></div><div><div><p><b><br>
Classification</b></p><p><b><br>
Salary Range</b></p></div><div><p><br>
Full-time, Exempt</p>
<p>$140,00&ndash;$166,250</p></div></div><br>
</div></div></div><p>
</p><p></p><p><b>POSITION OVERVIEW</b></p><p>
Common Sense Media is seeking a Data Scientist to help build data capabilities that allows the organization to answer the questions that matter most to advancing our mission and driving impact for kids and families. This is a hands-on, full-stack individual contributor role responsible for developing pipelines, models, and analyses that turn data from across the Common Sense Media ecosystem into reliable, decision-ready insights.</p><p>
The Data Scientist will develop predictive and statistical models that support organization-wide decisions, design and analyze experiments, apply causal inference methods, and lead impact analyses that measure how Common Sense Media's work affects kids, families, and educators, translating findings into clear, actionable recommendations for both technical and non-technical audiences. The Data Scientist's work will span the full range of data science at Common Sense Media, from foundational data engineering and data modeling through analytics, machine learning, experimentation, and impact analysis.</p><p>
As a senior technical voice on the team, the Data Scientist will help uplevel the organization's capabilities in data science, machine learning, and generative AI, advising on methodology, helping to develop and test AI product features, and raising the technical bar within product and engineering.</p><p>
</p><p><b>KEY RESPONSIBILITIES</b></p><p><i>
Analytics &amp; Modeling</i></p><ul><li><p>
Conduct rigorous exploratory and descriptive analyses that establish the foundational understanding of our audiences, programs, and performance.</p></li><li><p>
Develop predictive and statistical models that support organization-wide decisions.</p></li><li><p>
Apply modern machine learning techniques to ship models and analyses that drive impact.</p></li></ul><p><i>
Experimentation &amp; Causal Inference</i></p><ul><li><p>
Design and execute A/B tests and randomized controlled trials.</p></li><li><p>
Leverage rigorous quasi-experimental designs (e.g., difference-in-differences, regression discontinuity) and advanced causal inference techniques to evaluate the impact of complex product and program decisions where randomized testing is not feasible.</p></li></ul><p><i>
Impact Measurement</i></p><ul><li><p>
Lead rigorous impact analyses and program evaluations that measure how Common Sense Media's work affects kids, families, and educators.</p></li><li><p>
Apply causal inference and evaluation methods to measure real-world outcomes.</p></li></ul><p><i>
Insights &amp; Communication</i></p><ul><li><p>
Translate findings into clear, actionable recommendations for both technical and non-technical audiences.</p></li><li><h1 class="jobSectionHeader">
Partner closely with colleagues across Common Sense Media to put data to work across the organization.</h1></li></ul><p><i><br>
Analytics Engineering</i></p><ul><li>
Own the data models that turn raw data into clean, reliable, analysis-ready data sets used across the organization.</li><li>
Work with internal teams and external partners to establish data collection approaches, integrate new data sources, and ensure that incoming data is structured and trustworthy.</li><li><h1 class="jobSectionHeader">
Build and maintain the data pipelines and transformations necessary to support modeling, reporting, and impact measurement.</h1></li></ul><p><i><br>
Technical Leadership</i></p><ul><li><p>
Serve as a senior technical voice on the team, advising on methodology and raising the technical bar around how to use data in thoughtful decision-making.</p></li><li><p>
Help uplevel the organization's capabilities in data science, machine learning, and generative AI, supporting the development and testing of AI tools and features across the organization.</p></li></ul><p>
</p><p><b>QUALIFICATIONS</b></p><ul><li>
8+ years of experience in applied data science, analytics, or a closely related quantitative field, with a track record of shipping models and analyses that have changed decisions.</li><li>
Strong hands-on skills in SQL and Python or R, and expert use of standard machine learning libraries.</li><li>
Experience building and maintaining production data pipelines using modern cloud data warehouses (Snowflake, BigQuery, or Redshift) and modern data stack tooling (dbt, Airflow, Fivetran, or equivalents).</li><li>
Demonstrated experience designing and analyzing experiments, including A/B tests, quasi-experiments, and causal inference methods.</li><li>
Experience leading rigorous impact or program evaluation that measures real-world outcomes.</li><li>
Experience mentoring junior data scientists.</li><li>
Excellent written and verbal communication skills, with the ability to translate technical work into clear narratives for both technical and non-technical audiences.</li><li>
Demonstrated ability to serve as a senior technical voice within an organization, raising the technical bar of teammates and helping non-technical colleagues use data and AI tools effectively in their work.</li><li><h1 class="jobSectionHeader">
Strong understanding of data governance, privacy, and responsible data and AI practices, with particular sensitivity to data involving children and families.</h1></li><li><h1 class="jobSectionHeader">
Familiarity with product analytics, CRM, and marketing platforms (Amplitude, Google Analytics, Salesforce, Braze) and their underlying data structures is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience building predictive models for subscription, retention, lifetime value, or similar member or customer lifecycle problems is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience implementing and evaluating generative AI or LLM-based solutions, including prompt engineering, fine-tuning, retrieval-augmented generation, or self-hosted models, with rigorous evaluation against human or ground-truth baselines, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Experience designing AI evaluation frameworks, including human-in-the-loop evaluation, bias and fairness assessment, and ongoing monitoring of model performance, is a plus.</h1></li><li><h1 class="jobSectionHeader">
Prior experience in a mission-driven, nonprofit, education, or media organization is a plus.</h1></li></ul><p>
</p><p><b>WHAT WE OFFER</b></p><ul><li>
The opportunity to really make a difference in the lives of kids and families!</li><li>
A mission-driven team with a 23-year track record of impact for families and children.</li><li>
Competitive nonprofit compensation and comprehensive benefits.</li><li>
A collaborative, flexible work environment with meaningful leadership access and visibility.</li></ul><p>
</p><p><b>ABOUT COMMON SENSE MEDIA</b></p><p>
Common Sense Media is the leading nonprofit organization dedicated to improving the lives of kids and families by providing the research-backed information, education, and independent voice they need to thrive in the age of apps, algorithms, and AI. We rate, educate, and advocate for policies to protect and prepare kids online. Our ratings, research, and resources reach more than 150 million users globally, over 1.4 million educators, and more than 100,000 schools worldwide every year. Learn more at commonsense.org.</p></div><p></p>

Data Scientist

Data Scientist, Product Analytics

Pinterest

<div><div><p><b>About Pinterest:</b></p>
<p>
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.</p>
<p>
Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other's unique experiences and embrace the flexibility to do your best work. Creating a career you love? It's Possible.</p>
<p>
At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we're looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we'll explore your foundational skills and how you collaborate with AI.</p>
<p>
Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.</p></div><p>
Pinterest is seeking an experienced Staff Data Scientist to join our Finance &amp; Business Operations team. This is a hybrid data-science / applied-AI / product-engineering role inside Pinterest's CFO organization. It sits at the intersection of forecasting and finance analytics, internal tool-building, and AI adoption &mdash; and the person in it is expected to operate across all three.</p>
<p>
The core mandate is to make the CFO org's forecasting and planning work faster, be more rigorous, and more self-serve. In practice that has meant owning a forecasting product end to end (data pipeline through user-facing UI), partnering directly with Finance, BizOps, and Core/Monetization stakeholders to embed it in their workflows, and turning the company's emerging AI platform capabilities into tools that finance teams actually use day to day.</p>
<p>
This is a high-autonomy, high-trust individual-contributor role with broad cross-functional reach.</p><br>
<p></p>
<p><b>
What you'll do:</b></p>
<ul>
<li><b>Own forecasting tooling end to end.</b> Build and maintain the team's primary forecasting workbench &mdash; from the underlying data and forecast logic through the interactive web UI that planners use to create, adjust, and review forecasts. This spans baseline vs. adjusted forecast modeling, scenario/delta workflows, backtesting, and diagnostics (year-over-year and month-over-month seasonality, engagement rates, and similar).</li>
<li><b>Ship product, not just analysis.</b> Design and build user-facing features: chart and visualization work, guided onboarding, history/audit views, region and time-grain filtering, performance optimization, and the kind of polish and bug-fixing that makes an internal tool feel like a real product. Instrument usage (collect and analyze raw logs) and let adoption data drive the roadmap.</li>
<li><b>Drive AI adoption across Finance &amp; BizOps.</b> Take platform-level AI capabilities and turn them into concrete, trusted tools for finance users. Bring structured business cases (not wishlists) to platform/IT partners, pilot new capabilities, and write the enablement material &mdash; walkthroughs, documentation, &quot;where to get started&quot; guidance &mdash; that gets non-technical teams productive.</li>
<li><b>Stay ahead of the AI capability curve.</b> A significant part of this role is forward-looking: continuously read and interpret AI research (papers, model and tooling releases) and translate it into a grounded point of view on what will be possible in the next 6&ndash;12 months. Track the engineering roadmap closely, understand what platform capabilities are landing and when, and connect those dots to concrete opportunities for the CFO org &mdash; so the team builds for where AI is going, not just where it is today.</li>
<li><b>Set AI strategy and guide executives.</b> Turn that capability foresight into strategy: shape the CFO org's AI roadmap, prioritize where to invest, and advise senior leaders and executives on what's real, what's hype, and what to bet on. Communicate complex AI and technical trade-offs in plain, decision-ready terms, and act as a trusted technical advisor in executive conversations.</li>
<li><b>Deliver recurring finance analytics.</b> Support core CFO-org deliverables: budget-vs-actuals (BVAs), variance commentary, executive slide/deck preparation, and metric diagnostics (e.g., MAU and revenue diagnostics), including catching and resolving data-quality issues.</li>
<li><b>Partner broadly and communicate clearly.</b> Work directly with Finance, BizOps, Monetization, and platform/IT stakeholders. Translate ambiguous business questions into tooling and analysis, post clear release notes and stakeholder updates, and run live walkthroughs and training sessions.</li>
<li><b>Set technical and analytical standards.</b> raise the bar on rigor (validation, backtesting, sound metric definitions), make pragmatic build-vs-buy and scope calls, and create artifacts and documentation durable enough to outlive any single contributor.</li></ul><p><br>
<br>
<b>What we're looking for:</b></p>
<ul>
<li><b>Data science &amp; forecasting</b></li>
<li>Strong applied background in time-series forecasting and quantitative analysis: baseline construction, scenario/adjustment modeling, backtesting and forecast-accuracy evaluation, and seasonality analysis (y/y, m/m).</li>
<li>Fluency in turning messy business questions into well-defined metrics and diagnostics; rigorous about metric definitions, data quality, and validation.</li>
<li>Advanced SQL and proficiency in a primary analysis language (Python strongly preferred); comfort working directly with data warehouses and large datasets.</li>
<li><b>Engineering &amp; tool-building</b></li>
<li>Demonstrated ability to build and ship internal web tools, not just notebooks or one-off analyses &mdash; meaningful front-end / full-stack capability (e.g., JavaScript/TypeScript, modern UI frameworks, interactive data visualization).</li>
<li>Practical product-engineering instincts: UX/usability sense, performance debugging and optimization, handling state/data edge cases, and disciplined release hygiene (testing, build/lint, changelogs).</li>
<li>Experience building dashboards and self-serve analytics (e.g., Superset, Tableau, Looker, or equivalent).</li>
<li><b>Applied AI</b></li>
<li>Hands-on experience applying modern AI/LLM tooling to real workflows &mdash; prototyping with AI assistants, agentic/MCP-style tooling, or internal AI platforms &mdash; and a track record of moving from experiment to adopted tool.</li>
<li>Ability to build the business case for AI investment and to drive adoption with non-technical users (enablement, documentation, training).</li>
<li><b>AI foresight &amp; strategy (critical)</b></li>
<li>Demonstrated habit of staying current with AI research and the broader landscape: able to read papers and model/tooling release notes and form a credible, independent view of what will be feasible 6&ndash;12 months out.</li>
<li>Able to interpret an engineering roadmap and reconcile it with where the technology is heading &mdash; translating both into a concrete capability plan for the business.</li>
<li>Strong product/business strategy instincts: prioritizing AI investments, sequencing bets, and distinguishing durable capability from hype.</li>
<li><b>Executive influence</b></li>
<li>Proven ability to advise and guide senior leaders and executives on technical and AI strategy, and to make complex trade-offs legible to a non-technical executive audience.</li>
<li>Comfortable being the trusted technical voice in the room &mdash; framing decisions, managing expectations, and earning credibility with both finance leadership and engineering/platform partners.</li>
<li><b>Scope, ownership &amp; communication</b></li>
<li>Staff-level autonomy: can independently scope, prioritize, and deliver multi-month efforts with minimal direction, and make sound trade-off calls.</li>
<li>Excellent written and verbal communication; can write for executives and for end users, and can run live training and walkthroughs.</li>
<li>Strong cross-functional collaboration across finance, operations, and technical/platform partners.</li>
<li><b>Experience</b></li>
<li>Minimum of 8 years of relevant experience in data science, analytics engineering, or applied ML.</li>
<li>Bachelor's degree in a quantitative field (e.g., statistics, computer science, economics, engineering, math) or equivalent practical experience; advanced degree is a plus.</li></ul><br>
<p></p>
<p><b>
Relocation Statement:</b></p>
<ul>
<li>This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.</li></ul><br>
<p></p>
<p><b>
In-Office Requirement Statement:</b></p>
<ul>
<li>We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.</li>
<li>This role will need to be in the office for in-person collaboration1-2 times every 6-months and therefore can be situated anywhere in the country.</li><br>
</ul>
<p>#LI-NM4</p>
<p>
#LI-REMOTE</p><div><p><b>
Our Commitment to Inclusion:</b></p>
<div>
Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.</div>
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Staff Data Scientist, Finance & Business Ops

Google

<div>Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.<br>
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In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:<br>
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Health, dental, vision, life, disability insurance</li><li>
Retirement Benefits: 401(k) with company match</li><li>
Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment</li><li>
Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
</li><li>Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks</li><li>
Baby Bonding Leave: 18 weeks</li><li>
Holidays: 13 paid days per year</li></ul><br>
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: <b>San Francisco, CA, USA; Mountain View, CA, USA; New York, NY, USA; Washington D.C., DC, USA</b>.<h3 class="jobSectionHeader"><b>
Minimum qualifications:</b></h3><ul>
<li>Bachelor's degree or equivalent practical experience.<br>
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<li>3 years of experience designing, scoping, executing, and delivering research and analysis projects.<br>
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<li>Experience translating business problems into research questions and translating research findings and insights into marketing recommendations.<br>
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<li>Experience managing qualitative, quantitative and measurement agencies.<br>
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<li>Experience managing cross-functional or cross-team projects.</li></ul><br>
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Preferred qualifications:</b></h3><ul>
<li>Experience using AI tooling to conduct or facilitate research and measurement.</li>
<li>Knowledge of Washington DC and the US political environment.</li>
<li>Knowledge of policy in international and multinational (e.g. EU) policymaking processes.</li>
<li>Demonstrated interest in AI and its impact on society.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2>
In order to build excellent products and tell great stories, we must first profoundly understand our audiences - what motivates them, why they think and behave the way they do, and what they think of our brands and products. The Consumer Apps Marketing Research and Insights team is at the heart of developing this understanding.<div><p>
As a Research Manager, you will be embedded within the Reputation Marketing team, and inform how Google builds its reputation with the priority audiences shaping the AI landscape (policymakers, scientists, voters, and more). In this role, you will utilize both traditional research methods and new AI tooling to help the Reputation team understand and respond to the changing AI landscape via sharp, insights-driven marketing and communications.</p><p>
Your primary focus will be partnering with the AI Responsibility team: conducting and synthesizing brand tracking, qualitative research, and real-time social analysis across a variety of core issue areas (science, health, government, economy, and more). Your role will be to identify the audiences that matter in shaping the narratives for each issue, understand how those narratives spread and solidify, and provide tactical recommendations for how the AI Responsibility team should react. Managing stakeholder relationships, staying informed on Marketing priorities, scoping syntheses/studies to support strategy discussions, and managing a measurement framework will be an integral part of your day-to-day work.</p><p>
As a secondary-priority, you will help manage the global policymaker sentiment tracker. Your key responsibilities include helping to improve the program&rsquo;s operations, synthesizing findings and connecting dots between other programs, and presenting findings to teams across Google.</p></div>
Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems-from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can-changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
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US: $116000 - $167000 (USD) + 15% bonus target + equity + benefits<br>
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Learn more about benefits at Google.</div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Scope custom research from start to finish, including: designing an effective brief, managing Requests for Proposals (RFPs), onboarding research agencies, choosing a methodology, executing the study, and deploying insights and recommendations via compelling narratives in a variety of formats (decks, sites, emails, presentations, etc.).</li>
<li>Distill findings from a variety of sources into cohesive research narratives, ultimately delivering the underpinning insight that drives critical leadership decision-making.</li>
<li>Manage the global policymaker tracking program, synthesize insights from the research, and present to teams throughout Google.</li>
<li>Share research findings and insights via presentations in forums large and small, with everyone from junior to executive staff.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Research and Insights Manager, Marketing, Reputation and AI Responsibility

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