Traackr is a global SaaS technology company providing a data-driven influencer marketing platform that marketers use to optimize investments, streamline campaigns, and scale programs. Our customers range from some of the world’s largest companies in the beauty and personal care space to digitally native indie brands, which have all made influencer management and engagement a critical practice of their marketing and advertising programs. We are a remote-first company, and for the folks that like to meet in person, we have offices in San Francisco, New York, Boston, Paris, and London. We operate on a culture of mutual respect, with core value pillars including:

Trust. We earn the trust of our team, customers, creators, and partners through transparency, predictability, and integrity.

Diversity. Bringing diverse perspectives to the table results in stronger outcomes. All are welcome.

Value. Through our words and actions, we strive to create tangible value for our customers and peers. We only succeed when our community succeeds.

Ownership. We lead with action. We take pride in solving the hardest challenges and feel accountable for our commitments.

Mutual success. We share goals with each other and with our clients. Alignment, collaboration, and empathy are the cornerstones of our success.

This position is 100% remote, with the understanding that occasional in-person attendance may be required for trainings, meetings, and team gatherings, as determined by your manager.

Responsibilities

Partner with Product and Engineering to identify high-impact opportunities, frame ambiguous problems, define success metrics, and choose pragmatic approaches (heuristics, statistics, ML, or GenAI).
Lead rigorous experimentation across teams: hypothesis design, metric/guardrail definition, power analysis, A/B testing (or quasi-experiments), and clear readouts that drive decisions.
Build and iterate on ML/AI capabilities that ship to production (e.g., classification, information extraction, ranking/recommendations, and GenAI components such as RAG or developing the agent harnesses for our core agentic journeys), optimizing for value-added, latency, and cost.
Establish best-in-class evaluation practices for both ML and LLM features: golden datasets, offline/online evaluation plans, regression suites, and monitoring that catches quality drift early.
Enable engineers to build safely and effectively with AI by coaching on prompt patterns, tool/function calling, structured outputs, guardrails, and debugging/evaluation workflows.
Design and support agentic workflows where they add real product value, with clear constraints, observability, and fallbacks.
Support the end-to-end lifecycle of deployed models and AI systems: data requirements, training/fine-tuning where relevant, validation, deployment, monitoring, incident response, and continuous improvement.
Raise org-wide leverage by creating reusable assets (evaluation harnesses, shared datasets, templates, documentation) and running enablement workshops.
Communicate insights and tradeoffs clearly to technical and non-technical stakeholders, turning analyses into decisions and measurable impact.
Champion responsible, privacy-aware AI: appropriate data handling, bias/fairness considerations where applicable, and human-in-the-loop workflows when needed.

Core Qualifications

3+ years (or equivalent) delivering data science work that shipped to production and/or materially influenced product direction
Experience collaborating cross-functionally and communicating clearly with diverse stakeholders; ability to influence without authority
Strong Python and SQL skills, with the ability to write maintainable, production-quality code (testing, reviews, documentation)
Demonstrated mentorship/enablement—helping other engineers and teams adopt best practices and ship faster with higher quality

At least 3 of

Strong applied statistics and experimentation skills (A/B testing, causal thinking, metric design, interpretation under uncertainty)
Proven ability to evaluate and improve models in real conditions: dataset design, error analysis, offline metrics, online measurement, monitoring, and iteration
Hands-on experience building with LLMs in product contexts, including some of: RAG/grounding, tool/function calling, structured outputs, prompt iteration, quality/cost/latency tradeoffs
Practical approach to LLM evaluation: golden sets, regression testing, human review loops, and monitoring for quality drift
Experience with modern MLOps/LLMOps practices (experiment tracking, ETL pipelines, versioning, CI/CD for ML, observability)
NLP and information extraction/classification on noisy social/content data
Experience developing and evaluating large scale Retrieval, Recommendation- and Search Systems.

Strong plus

Experience with large scale data and analytics platforms
Experience with Spark or other distributed computing
Experience with data lake technologies such as Databricks or equivalent
Experience with cloud providers such as AWS or equivalent

Benefits

Competitive Salary
Remote Work Options with Hybrid Flexibility and Home Office Set-Up Stipend
Coworking Office Subscription for Collaborative Spaces
Health, Dental, and Life Insurance Coverage*
Open Vacation Policy and Flexible Holiday Schedule to Suit Your Needs
Paid Parental Leave to Support Quality Time with Your Loved Ones
Career Development, including Internal and External Training Opportunities