About impact.com

impact.com is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates, impact.com empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—Performance (affiliate), Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust, impact.com helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on impact.com to power more than 225,000 partnerships that deliver measurable business results.

 

About the Role

We’re seeking a Senior Data Scientist to build and scale machine learning systems that understand publisher and creator content and turn it into high-quality signals for targeting, search, ranking, recommendation for yield optimization. You’ll work on core problems at the intersection of NLP, recommendations, and multimodal understanding—spanning publisher content intelligence, content-based targeting, multimodal search, and page ranking at scale.

This role is hands-on and end-to-end: you’ll own modeling and experimentation work from problem framing through productionization in partnership with Business Stakeholder, Engineering, Product, and MLOps. A key medium-term focus will be improving and upscaling our proof of concept for product detection in creator posts (image + text), enabling richer downstream experiences across search, targeting, and recommendations.

Core Responsibilities

Publisher content intelligence & NLP

• Build NLP models to classify, summarize, and extract structured signals from publisher pages and creator posts (topics, entities, intent, brand/product mentions, sentiment where relevant).
• Develop robust content embeddings and semantic similarity systems for retrieval, clustering, and taxonomy/category modeling.
• Create scalable evaluation frameworks for content models (gold sets, weak supervision, human-in-the-loop labeling, error analysis).

Content-based targeting & relevance signals

• Turn content signals into targeting features that improve advertiser/publisher matching, relevance, and performance.
• Define and maintain feature sets that are reliable at scale (coverage, latency, cost), resilient to drift, and aligned with privacy and platform constraints.
• Collaborate with Product to translate targeting goals into measurable model outcomes (lift, precision/recall, coverage, monetization impact).

Recommendations, ranking & multimodal retrieval

• Apply recommender and ranking techniques to improve discovery and relevance across content and product surfaces.
• Build and tune ranking signals for page ranking at scale, incorporating quality, relevance, and engagement proxies where appropriate.
• Contribute to multimodal search pipelines (text + image) using embeddings and candidate generation/reranking approaches.

Multimodal product understanding (key medium-term focus)

• Improve and scale our PoC for product detection in creator posts: enhance accuracy, expand category coverage, reduce operational friction, and harden the pipeline for production use.
• Partner with Engineering to productionize image-based product signals (detection/classification), integrate them with text signals, and make outputs usable for downstream search/targeting/ranking systems.
• Lead systematic iteration loops: dataset improvements, labeling strategies, model retraining, threshold tuning, and failure-mode analysis.

Experimentation & measurement

• Design offline metrics and online experiments (A/B tests, holdouts, interleaving where relevant) to quantify impact and guide tradeoffs.
• Build monitoring for model quality and system health: drift detection, coverage, performance regressions, and alerting.
• Communicate results clearly and drive decisions through crisp narratives and dashboards.

Production delivery & cross-functional collaboration

• Own end-to-end ML delivery: problem definition, data/feature design, model development, evaluation, launch planning, and iteration.
• Collaborate with MLOps/Data Platform to ensure reproducibility, observability, and reliability (testing, deployment patterns, SLOs).
• Create strong documentation and enablement for stakeholders consuming content signals and models.

Qualifications

Required

• 5+ years of experience in data science / applied ML, with a proven track record of shipping production models with measurable impact.
• Strong Python and SQL skills; experience working with large-scale data and distributed compute (Spark/Databricks or equivalent).
• Deep experience in NLP, including at least two of: text classification, information extraction, embeddings/semantic similarity, taxonomy/category modeling, robust evaluation.
• Experience applying recommendation or ranking methods (candidate generation, learning-to-rank, retrieval + reranking, implicit feedback signals).
• Strong experimentation skills: design, analysis, interpretation, and stakeholder communication of A/B tests or quasi-experiments.
• Ability to operate in ambiguity and drive work end-to-end with minimal oversight.

Preferred / Nice to have

• Experience with multimodal ML (vision + language), including image classification/detection or product recognition in UGC/creator content.
• Experience with search/retrieval systems, vector search/vector databases, and relevance evaluation frameworks.
• Familiarity with modern deep learning tooling (PyTorch/TensorFlow) and model serving patterns.
• Experience building human-in-the-loop pipelines (labeling workflows, active learning, QA sampling).
• Familiarity with GCP (Vertex AI, BigQuery, Cloud Run) and/or mature MLOps practices (CI/CD for ML, monitoring, drift).

What Sets You Apart

• You’re rigorous about measurement but pragmatic about shipping—able to deliver MVPs and evolve them into durable systems.
• You have strong product instincts: you connect modeling choices to real user outcomes and business impact.
• You’re comfortable with messy content and edge cases, and you excel at error analysis and iterative improvement.
• You can influence without authority, bring clarity to ambiguity, and help cross-functional teams align on tradeoffs.

 

Benefits and Perks:

At impact.com, we believe that when you’re happy and fulfilled, you do your best work. That’s why we’ve built a benefits package that supports your well-being, growth, and work-life balance.

• Flexible Working: Our Responsible PTO policy means you can take the time off you need to rest and recharge. We're committed to a positive work-life balance and provide a flexible environment that allows you to be happy and fulfilled in both your career and your personal life.
• Health and Wellness: Your well-being is a priority. Our mental health and wellness benefit includes up to 12 fully covered therapy/coaching sessions per year, with additional dependent coverage. We also offer a monthly gym reimbursement policy to support your physical health.
• A Stake in Our Growth: We offer Restricted Stock Units (RSUs) as part of our total compensation, giving you a stake in the company's growth with a 3-year vesting schedule, pending Board approval.
• Investing in Your Growth: We’re committed to your continuous learning. Take advantage of our free Coursera subscription and our PXA courses.
• Parental Support: We offer a generous parental leave policy, 26 weeks of fully paid leave for the primary caregiver and 13 weeks fully paid leave for the secondary caregiver.
• Technology Financial Support: We provide a technology stipend to help you set up your home office and a monthly allowance to cover your internet expenses

impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.