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# Data Scientist

- **Company:** Data Inc
- **Location:** United States (Remote available)
- **Salary:** $35,360.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Big Data, Data as a Services, Software Debugging, Fraud Prevention and Detection, Apache Hive, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Apache Spark, Pandas, Information Technology, Xgboost, Apache Kafka
- **Published:** August 24, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pecdsxifyb

## About the Role

Bachelor's degree or higher in Computer Science, IT, Software Engineer, Software Development, Actuarial Science, or a related quantitative field.

Great communication skills, strong problem-solving capabilities, and a desire to learn.

Strong knowledge of Machine learning and statistics.

Strong debugging, analytical, and problem-solving abilities, excellent team player with the ability to maintain strong internal and external relationships.

Working Python (pandas, scikit-learn) and SQL. You should be comfortable pulling and reshaping data yourself.

Experience with credit scoring or financial service models is a plus.

Nice to have

Any exposure to credit scoring, fraud, or financial services modelling.

Familiarity with scorecard concepts: WOE/IV, Gini/KS, PSI, reject inference.

Exposure to gradient boosting libraries (XGBoost, LightGBM, CatBoost).

Exposure to big data or pipeline tooling - Spark, Hive, Kafka, Airflow. Nice to see, not expected at this level; we will teach you our stack.

D. BENEFITS

Challenging problems to solve with high impact to the society.

High performing team members with strong background.

Great problems to solve, great team to work with.

## Description

We are looking for innovative data scientists to build and evolve our leading financial products. You will work with a team of young and talented scientists and engineers to build models on robust and scalable data big data pipelines, conduct advanced research in implementing state-of-the-art ML and AI techniques, build high-quality data services that are both real-time and batch-based.

B. RESPONSIBILITIES

Develop and validate machine learning models for credit scoring, fraud detection and lead scoring.

Engineer features from behavioural, telco and transactional data sources.

Monitor deployed models and data feeds; identify anomalies and drift that affect production performance.

Collaborate with Business owners to define and design a roadmap for financial products.

Enhancing data collection to integrate information that is relevant to products.

Perform analysis and draw meaningful insights from company's data sets; visualize and present those insights to stakeholders.


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