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.
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
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
Traackr employs individuals in multiple US states and countries. We use market benchmark data and geographic zones to determine our salary ranges. Your zone's specific pay range is dependent on your home location. We encourage you to discuss your zone-specific pay range with your Traackr recruiter for more details.
Benefit programs vary by country/state of residency, are subject to eligibility requirements, and may be modified from time to time. Ask for more details about the benefits in your specific region.
Traackr is an Equal Employment Opportunity employer. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other legally protected characteristics. All your information will be kept confidential in accordance with EEO guidelines.
Unsolicited resumes
Traackr does not accept unsolicited resumes/CVs from headhunters or recruiting agencies sent directly to Traackr employees or through our website. Traackr will not pay any fees to any third-party agency or company unless there is a signed agreement with Traackr.
Privacy
Traackr, Inc. has published a Privacy Notice, including CCAP for California and GDPR policies for its UK and European Union subsidiaries, accessible at https://www.traackr.com/privacy-policy.
All questions, comments, and requests regarding data processing at Traackr should be addressed to
[email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
