Senior Data Scientist

<div class="content-intro"><h2><strong>About Invisible</strong></h2> <p>Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most.</p> <p>Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.</p> <p>Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets.</p> <p>Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology.</p></div><h2><strong>About The Role</strong></h2> <p>As a Senior Data Scientist, you’ll work directly with clients and our internal delivery teams to build and deploy AI-powered solutions that transform how work gets done. You’ll own projects end-to-end: scoping ambiguous problems, prototyping AI workflows, and deploying scalable data science solutions on top of our products — all while interfacing with technical and non-technical stakeholders.</p> <p>This is a hybrid role: equal parts data scientist and technical consultant. It’s perfect for an experienced data scientist who wants to stay hands-on, lead high-impact work, and stay close to the results it generates.</p> <h2><strong>What You'll Do</strong></h2> <ul> <li>Lead the scoping, development, and deployment of end-to-end statistical and ML solutions to complex operational problems, partnering with delivery leaders and clients</li> <li>Work directly with clients to frame ambiguous business challenges, explore solution paths, and translate technical insights into measurable results</li> <li>Build, test, and deploy machine learning models that operate reliably in dynamic production environments</li> <li>Explore and analyze large datasets from diverse sources to inform model design and evaluate performance under realistic constraints</li> <li>Design and run experiments (including A/B tests) to measure and improve product and business performance</li> <li>Partner with software engineers and fellow data scientists to integrate models into deployable systems</li> <li>Build reusable tooling and infrastructure that accelerates future model development and deployment</li> <li>Mentor other data scientists and help set best practices for code quality, experimentation, and model evaluation</li> </ul> <h2><strong>What We Need</strong></h2> <ul> <li>6+ years of hands-on experience in data science, including client-facing or forward-deployed work</li> <li>A Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field, or equivalent professional experience</li> <li>Strong proficiency in Python and common ML/data libraries (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, etc.), along with strong SQL skills</li> <li>A proven track record of taking models from prototype to production</li> <li>Solid grasp of statistical modeling, experimental design, model evaluation, and data debugging</li> <li>Experience working with large-scale datasets using distributed tools such as Spark</li> <li>Comfort navigating cloud environments (GCP, AWS, or similar)</li> <li>Strong code hygiene: you write clean, modular, testable code in collaborative environments</li> <li>Strong communication skills — you can explain data decisions and build trust with technical and non-technical stakeholders</li> <li>Entrepreneurial mindset and comfort working in fast-moving, unstructured environments</li> <li>Willingness to be on-call for our customers when situations arise</li> </ul> <p><strong>Bonus (Nice to Haves):</strong></p> <ul> <li>Familiarity with MLOps practices (model tracking, versioning, reproducibility)</li> <li>Experience contributing to multi-model systems or pipelines built by larger teams of data scientists and engineers</li> </ul> <p> </p> <h2><strong>What’s In It For You</strong></h2> <p>Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.</p> <p>The annual salary range for this position, depending on level, is: <strong>$193,000 - $250,000 USD</strong></p><div class="content-conclusion"><p>You can find more information about our geographic pay tiers <strong><a class="c-link" href="https://invisibletech.ai/compensation-and-pay-tiers" target="_blank" data-stringify-link="https://invisibletech.ai/compensation-and-pay-tiers" data-sk="tooltip_parent">here</a>.</strong> During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.<br><br>*Bonuses and equity are included