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.
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.
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.
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
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.
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
Other open roles at Traackr(2)
Discover Traackr’s AI-powered influencer marketing platform. Find creators, manage campaigns, and measure results with creator intelligence and AI workflows.
Key team members
Pierre-Loïc Assayag
Derek Pilling
Peter Harrison
Dave McLean
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