Job Specifications
We are looking for a Sr. Data Scientist to join our growing Data team in our Product organization. This is a senior individual contributor role with broad scope across product insights, experimentation, modeling and applied analytics. The ideal candidate is not only technically strong, but also proactive in uncovering opportunities, framing insights, and ensuring leaders have the context they need to make the right calls. We are looking for this individual to bring analytical thought leadership as we continue to rapidly grow and iterate on our product offerings across Talkiatry.
You'll own projects that range from producing growth and performance analyses that guide strategic and product decisions, to designing and evaluating experiments, to building predictive models, and to improving patient engagement and clinical efficiency. You'll be expected to communicate insights proactively, frame results in business terms, and work closely with your core cross-functional teammates (Product, Engineering and Design) as well as stakeholders across Operations, Clinical, and Finance.
About Talkiatry:
Talkiatry transforms psychiatry with accessible, human, and responsible care. We're a national mental health practice co-founded by a patient and a triple-board-certified psychiatrist to solve the problems both groups face in accessing and providing the highest quality treatment.
60% of adults in the U.S. with a diagnosable mental illness go untreated every year because care is inaccessible, while 45% of clinicians are out of network with insurers because reimbursement rates are low, and paperwork is unduly burdensome. With innovative technology and a human-centered philosophy, we provide patients with the care they need--and allow psychiatrists to focus on why they got into medicine.
You will:
Proactive Insights & Storytelling
Identify growth opportunities for the Product teams by analyzing data and understanding patient behaviors
Help Product teams maximize impact, by working wtih PMs and engineers to understand costs and constraints, while looking at underlying data and making reasonable assumptions to size opportunities
Identify risks by exploring data and monitoring trends to understand the impact of emerging issues before they become problems
Frame analyses and estimates in terms of "so what" for the practice to ensure findings don't just inform, but influence the team
Develop regular deliverables that turn historical data into forward looking guidance for product and operational teams
Anticipate questions our teams should be asking and bring forward insights before they're requested
Modeling & Prediction
Build, evaluate, and productionalize models to forecast outcomes such as patient retention, no shows, demand patterns, and more
Balance technical sophistication with pragmatic application, recognizing when simpler analyses can achieve the same business impact
Experimentation & Measurement
Partner with Product and Operations teams to design experiments, define success metrics, and implement robust statistical evaluation frameworks
Ensure appropriate statistical power, so results are both valid and actionable
Analyze experiment results and establish clear post experiment communication, ensuring learnings (positive or negative) are codified and inform future product or operational decisions
Applied Analytics
Lead analyses on patient growth, clinician utilization, marketplace dynamics and more to surface actionable insights to leadership
Translate adhoc analyses into repeatable frameworks and scalable reporting so insights don't stay one-off, but become institutional knowledge
Collaboration
Collaborate with Data Engineering and BI teams to define requirements and highlight gaps, so that data pipelines reliably support advanced analytics and modeling
Act as a thought partner to Operations, Clinical, and Finance leaders, not just answering questions, but shaping the questions we should be asking
You have:
4+ years of experience in data science, analytics, or related fields, with a track record of delivering measurable business impact
Advanced proficiency in Python (pandas, scikit-learn, statsmodels and all other common data science packages) and SQL; experience with modern data warehouses (Snowflake, Redshift, BigQuery)
Strong foundation in experimental design, causal inference, and statistical methods
Practical understanding of ML modeling trade-offs: when to deploy advanced models vs. when a well-structured analysis is the right tool
Understands the spirit of George Box's reminder that "all models are wrong, but some are useful" and applies that judgment to build solutions that balance sophistication with pragmatism
Exceptional communication skills, displaying the ability to frame technical findings in a way that resonates with stakeholders
Experience in DBT is a plus, but not required
Experience on a Product growth team, where experimentation and opportunity identification were key aspects of the role, is a plus