Data Science · Machine Learning Research
Vaibhav
Vennam
I build data systems, machine-learning models, and numerical methods, then open them up so people can see how they think.
TOP SIGNALS
Michigan State University
GPA: 4.0
CAREER TRAJECTORY
Professional Experience
Three roles spanning systems engineering, decision science, and applied AI research.
PROJECTS
Selected Projects
Five investigations across rare events, numerical computing, optimization, language models, and climate intelligence.
RILA Cap-Rate Modeling
A forecasting pipeline for understanding when annuity cap rates will change and how large the next change may be.
Before the data
What is RILA?
A Registered Index-Linked Annuity is an insurance product whose return is tied to a market index, with limits that provide some protection against losses. Its cap rate helps determine how much of an index gain a customer can receive.
This project studied competitors' cap rates over time. The practical question was whether a rate would change next and, if it did, how large that change would be.
Data inspection
Inspecting the raw cap-rate history
Before changing anything, inspect several product segments side by side. Most contain plausible rates mixed with occasional placeholders. Segment 1464 shows the extreme case: every recorded rate is 9.99.
Only the Rate column carries placeholders. This scene observes the issue without replacing any values.
86,000 rows, 143 columns, 1,530 pricing segments. Every segment is one product line tracked over time.
DATA PREPARATION
Preparing the cap-rate history
Step A: remove segments dominated by placeholders
Step A does not repair anything. It measures how much of each segment is placeholder, and removes the segments where that share is too high. The histogram shows why the threshold is set where it is.
At 10% the cut is conservative: almost every segment already sits in the first bar, so the rule removes 104 clearly unreliable segments and keeps 93.2% of the data.
Why 10% and not 50%? Because the data says so: the vast majority of segments are under 10% placeholder, so anything above that line is an outlier, not a normal series.
Feature engineering
Building features without letting the answer leak in
TARGET
Did the cap rate change today?
FEATURE
Days since last change
Counting days since the last change resets to zero on the very day a change happens, which gives the model information it should not have at prediction time. Shifting the counter back by one day removes that. On top of it, each segment and each category (carrier, state, product type, index, protection option, crediting type, term) gets a behavioral profile: how often it moves, how big its moves are, how wide its range is.
The repaired counter became the most important feature in the final model. Left open, it would have produced inflated scores with no real predictive value.
Modeling
Two models, because movement is rare
INPUT
1,143 segments, split 80/20 in time inside each segment
STAGE 1 · CATBOOST CLASSIFIER
Will the rate move tomorrow?ROC-AUC 0.911 · PR-AUC 0.608 · recall 80% at a 0.20 threshold
NO · 98.1%
The rate is carried forward unchanged
YES · 1.9% → STAGE 2 REGRESSOR
By how much?RMSE 0.039 against a do-nothing baseline of 0.124 · R² 0.68
Moves happen on roughly 1.9% of days, somewhere between one in fifteen and one in three hundred and sixty five depending on the product. The rare class is weighted about 51 times heavier so the classifier cannot win by always saying no.
Top signals for the classifier: days since the last change, how often that segment moves, and corporate bond yields and spreads. For the size of the move: yesterday's rate and the segment's largest historical move.
Historical validation
One-step accuracy without compounding drift
TEACHER FORCING · WHAT I USED
WHY TEACHER FORCING
In historical backtesting, the actual rate is known after each prediction. Feeding that confirmed value into the following step prevents one error from affecting the rest of the sequence.
In use, a predicted change can trigger a data check. The confirmed rate then becomes the input for the next day's prediction.
SKILLS
Technical Skills
Methods, tools, and research areas across engineering, data science, and AI.
Demonstrated skills include the roles and projects where they were applied.
Discipline 01
Programming & Engineering
Demonstrated
Additional knowledge
Discipline 02
Machine Learning & Modeling
Demonstrated
Additional knowledge
Discipline 03
Statistics & Causal Inference
Demonstrated
Additional knowledge
Discipline 04
AI/GenAI
Demonstrated
Additional knowledge
Discipline 05
Cloud & Data Infrastructure
Demonstrated
Additional knowledge
Discipline 06
BI & Visualization
Demonstrated
Additional knowledge
Discipline 07
Applied Math & Optimization
Demonstrated
Discipline 08
Research & NLP
Demonstrated
Discipline 09
Business & Analytical
Demonstrated