Data in. Clarity out.
Sinhara Analytics partners with pharma, actuarial teams, and data-heavy organizations— from labs to small businesses—that need rigorous statistics and probability modeling, not black boxes.
Your data shouldn’t sit in a spreadsheet. We turn it into decisions.
Led by two PhD statisticians with experience in probability models, variable selection, sample size planning, assay data, actuarial-style risk modeling, and complex analytics in R.
Sinhara Analytics is a husband–wife statistics team based in North Carolina. We bring together years of teaching, research, and consulting to help teams frame the right questions, choose appropriate methods, and explain results in plain language.
Our work spans probability modeling, regression, variable selection, and data exploration in pharma, actuarial, and other data-rich environments. We are comfortable working with scientists, analysts, actuaries, decision-makers, and small-business owners—and we focus on solutions that are statistically sound and operationally useful.
Dr. Hasthika Rupasinghe · Statistical modeling, variable selection and penalized regression, probability modeling, and machine learning in R. Hasthika has passed SOA Exam P (Probability) and SOA Exam SRM (Statistics for Risk Modeling).
Dr. Lasanthi Watagoda · Variable selection, unsupervised learning (e.g., clustering, PCA, dimension reduction), study design, variability assessment, and interpretation and communication. Lasanthi has passed SOA Exams P, FM, and SRM.
Together, we provide end-to-end support—from “we just have a spreadsheet” to “we need a defensible analysis and clear recommendations.”
We work on both narrowly defined analyses and open-ended “we have data and questions” problems. Arrangements can be project-based, hourly, or ongoing, and we’re happy to support both larger organizations and small businesses.
Support for probability-driven questions, especially in pharma and actuarial settings.
Modern regression and modeling, implemented with reproducible R workflows.
Help at the planning and “messy data” stages—before, during, or after a study or project, for both established teams and small businesses.
These are some of the settings where our blend of probability, modeling, and data skills tends to be especially useful—including work with pharma, actuarial teams, and data-rich small businesses.
We emphasize visualizations that make variability, uncertainty, and differences easy to see—whether that’s distributions, dose–response relationships, model comparisons, or patterns in data from small or large teams.
Whether you have a fully specified question or just “a lot of data,” we follow a structured process with room for discussion and iteration. This applies equally well to pharma projects, actuarial work, and small-business analytics.
We review your goals, your data (or planned data), and your timelines to see if we’re a good fit and what level of support makes sense.
You receive a written scope of work, including questions to be addressed, methods we anticipate using, a timeline, and fees.
We carry out the analyses in R, sharing interim results and discussing modeling choices, assumptions, and alternative scenarios as needed.
You receive a clear summary of findings, supporting figures and tables, and (when appropriate) R code or scripts, plus time for Q&A with your team.
Representative comments from teams we have supported. Full references available on request.
Sinhara Analytics helped us frame the right probability questions and interpret the results in a way our scientists and leadership could act on. Their work strengthened our decisions and our confidence in them.
They took a messy, high-dimensional dataset and turned it into clear, defensible findings. We appreciated both the rigor of the analysis and the clarity of the explanations.
Share a brief description of your data (or planned data), your key questions, and any timelines. We work with pharma and biotech teams, actuarial groups, and data-driven small businesses. We’ll respond with suggested next steps and, if appropriate, a time for a short introductory call.