Sr. Analytics Engineer

About us

At Koltin, we're redesigning the way we care for our moms, dads, and grandparents to help them remain healthy and independent for as long as possible.

How do we do it? Through our health memberships, which provide personalized, preventive care to support our members' long-term well-being.

We are the first company in Mexico offering health memberships that include major medical insurance coverage for older adults—up to 84 years old—backed by BBVA Seguros Salud.

About the role

The Quantitative Risk Analyst at Koltin applies statistical thinking to health and insurance data—turning clinical records, lab results, diagnoses, and claims into rigorous risk insights that drive both prevention strategy and financial decision-making.

This is a role for someone who is genuinely fascinated by how data reveals patterns in human health—and who understands that predicting a member's risk is ultimately about preventing a bad health outcome, not just estimating a cost. You'll work on questions like: Which members are most likely to develop a chronic condition in the next 12 months? What biomarkers from our longevity study best predict hospitalization? Are our prevention programs actually changing trajectories? How do claims patterns differ across risk cohorts?

You will collaborate closely with our clinical team, Data Scientist, and actuaries to build risk models grounded in clinical evidence—models that shape prevention protocols, clinical routing, and pricing strategy.

Ideal backgrounds: Statistics, Biostatistics, Epidemiology, Actuarial Science, Mathematics, Quantitative Biology, Public Health (with strong quantitative component). The ideal candidate is someone who loves the statistical side of health problems—understanding distributions, modeling time-to-event outcomes, and quantifying uncertainty in a clinical context.

You're a good fit if you:

• Identify with our Values: Ownership, Collaboration, Excellence, Data-driven, Curiosity.
• Are genuinely drawn to the intersection of statistics and health: you find survival curves, risk stratification, and biomarker distributions intrinsically interesting.
• Think in populations, not just individuals: you want to understand how cohorts of patients evolve over time, not just how one person compares to average.
• Are statistically rigorous: you don't just report a mean—you think about the distribution, the sample size, the confidence interval, and what assumptions are baked in.
• Know when a result is too good to be true—and have the instinct to investigate rather than celebrate.
• Can communicate statistical uncertainty clearly to clinicians and non-statisticians, without dumbing down the substance.
• Are curious about preventive medicine as a domain: you want to understand how early intervention changes health trajectories and financial outcomes.
• Are collaborative: you work well with doctors, actuaries, and data engineers, even when they think differently than you do.
• Are comfortable in a startup environment where data definitions evolve, tools change, and not everything is documented.
• Communicate well in Spanish & English.

Had experience with:

• Statistical modeling in Python or R: GLMs, survival models, mixed effects models, regularization techniques.
• Survival analysis applied to health or clinical data: Kaplan-Meier, Cox proportional hazards, accelerated failure time models.
• Risk stratification or clinical segmentation: grouping populations by risk profile using statistical or ML approaches.
• Biostatistics or epidemiological methods: incidence rates, relative risk, hazard ratios, propensity score matching.
• Claims or health data analysis: understanding frequency/severity patterns, cost drivers, cohort differences.
• SQL for data extraction and preparation from health or operational databases.
• Communicating quantitative findings clearly to clinical or business stakeholders through tables, charts, and written summaries.
• (Nice to have) Experience with electronic health records (EHR), lab data, or clinical datasets.
• (Nice to have) Knowledge of actuarial concepts (loss ratios, pricing basics, experience studies)—not necessarily exam-level, but conceptually comfortable.
• (Nice to have) Familiarity with causal inference methods (difference-in-differences, instrumental variables, synthetic control) for evaluating intervention effectiveness.
• (Nice to have) R packages: survival, lme4, ggplot2, or Python equivalents (lifelines, statsmodels, scikit-survival).
• (Nice to have) Experience working with public health or insurance datasets in Mexico or Latin America.

Key Outcomes:

• Clinical risk models are built and maintained: predictive scores for hospitalization, chronic disease onset, or health deterioration—validated out-of-sample and monitored over time.
• Prevention program effectiveness is quantified: statistical analyses that isolate the impact of Koltin's interventions on health outcomes and claims frequency.
• Risk segmentation is backed by statistical evidence: cohort definitions grounded in biomarkers, diagnosis patterns, and longitudinal behavior—not just business rules.
• Survival analyses of health events inform clinical routing: time-to-event models that help the clinical team decide when and how to intervene.
• Claims patterns are understood at a cohort level: frequency and severity differences by age band, condition, and risk profile are documented and communicated.
• Actuaries and clinical leadership have reliable statistical inputs that improve the quality of pricing, prevention, and operational decisions.
• Methodologies are documented so analyses can be reproduced, audited, and built upon over time.

Perks & Benefits:

• 🎓 Access to professional development tools and resources (courses, books, workshops, etc.)
• 💊 Unlimited virtual medical assistance (general practitioner, nutritionist, psychologist) for you and 3 family members
• 🏈 Access to physical wellness tools (TotalPass)
• 🌴 9 extra vacation days per year in addition to the legal minimum
• 👓 Private Major Medical Insurance
• 💻 All the equipment you need to do your best work
• 💵 $700 MXN monthly support for home office expenses
• 💎 One-time $2,500 MXN support to set up your home workspace

Being aware that some groups don't apply even if they are qualified, this is a reminder to apply even if you think that you don't tick all the boxes!

*Advanced Spanish proficiency is strongly preferred for this role.