# Kendra Obika

Kendra Obika is a remote worker on Himalayas, Data Scientist at Nasdaq, based in United States.

Source: https://himalayas.app/@kendraobika

```text
Data Scientist at Nasdaq who cut manual review time by 70% with an entity resolution system.
```

| Detail | Value |
| --- | --- |
| Availability | Looking for a job |
| Location | United States |
| Authorized to work in | United States |
| Salary expectations | 100k+ USD |
| Job categories | Data Scientist, Machine Learning Engineer, Data Engineer |
| Skills | Data Science, Machine Learning, Entity Resolution, Large Language Models, RAG, Natural Language Processing, Graph Databases, Vector Search, Statistical Modeling, Bayesian Modeling, A B Testing, Feature Engineering, Model Evaluation, Model Deployment, Model Monitoring |

## About

```text
At Nasdaq, I architected Databricks ML pipelines and developed an entity resolution system that reduced manual review time by about 70%. I also automated data-cleaning workflows, eliminating more than 150 hours of monthly manual work across two internal teams.

At RevealMe, I architected a privacy-first LLM-powered knowledge graph and developed hybrid graph and vector retrieval methods, improving retrieval precision by about 35%. I also led development of an end-to-end ML system for entity extraction and embedding generation, reducing memory processing latency by about 50%.
```

## Experience

### Data Scientist (current)

[Nasdaq](https://himalayas.app/companies/nasdaq). Feb 2023 - Present

```text
Architected Databricks ML pipelines and developed a classification-based entity resolution system, reducing manual review time by ~70%. Engineered AWS-backed workflow automation and deployed a production ML system for automated Salesforce case resolution.
```

Skills: Databricks, Spark, SQL, AWS, Lambda, Appsmith, Salesforce, Classification, Entity-Resolution, ML-Pipelines, Automation

### Lead Data Scientist (Part-Time)

RevealMe. Jul 2025 - Feb 2026

```text
Architected a privacy-first LLM-powered knowledge graph using Neo4j and pgvector, improving retrieval precision by ~35%. Developed a graph-based relevance ranking model and led development of an end-to-end ML system for entity extraction and embedding generation.
```

Skills: LLM, Neo4j, Pgvector, Graph-Databases, Vector-Search, Entity-Extraction, Embeddings, Retrieval, Ranking, ML-Systems

## Education

### University of Texas at Austin

Master of Science, Data Science. Grade: 4.0/4.0

```text
Pursuing a Master of Science in Data Science with an expected graduation in May 2027. Coursework includes machine learning, natural language processing, and advanced deep learning.
```

### Cornell University

Bachelor of Science, Biometry and Statistics. Grade: 3.7/4.0

```text
Bachelor of Science in Biometry and Statistics with a concentration in Computational Data, completed in December 2022.
```

## Tech stack

- Apache Spark
- AWS IAM
- Amazon S3
- Salesforce
- PostgreSQL
- Databricks
- Neo4j
- Terraform
- Python
- Appsmith
- TensorFlow
- PyTorch
- scikit-learn
- FastAPI
- AWS Lambda
- Docker
- SQL
- XGBoost
- CatBoost
- pgvector
- AWS

To contact Kendra, message them from this profile with a Himalayas employer account: [Hire on Himalayas](https://himalayas.app/hire).

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