Data Scientist

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.

Data is a crucial part of Traackr’s cutting‑edge SaaS influencer marketing platform. We collect, organize and interpret terabytes of data about brands and influencers. Our solutions help our brands build and maintain their influencer portfolios, understand how their campaigns affect the information landscape, and optimize their influencer marketing spend.

As a Senior Data Scientist, you will help push Traackr’s product and business forward by turning ambiguous customer problems into measurable outcomes. You’ll partner closely with Product and Engineering to design experiments, develop and evaluate ML, AI, Statistics, and recommendation capabilities (from classic ML models, to LLM‑powered automations, to large‑scale search capabilities). As a Data Scientist at Traackr you will establish reliable practices for evaluation, monitoring, and continuous iteration.

You’ll also raise the bar across teams by coaching others on experimentation and AI evaluation best practices.

• 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,…