Data Scientist

Why Hudson Data

Work on real data problems across fintech, lending, and AI-ops platforms.

Exposure to full-stack analytics: modeling, automation, and product integration.

Flat structure, high ownership, and visible impact on business outcomes.

Continuous learning and mentorship from experienced data and product leaders.

Role & responsibilities

Hudson Data is a fast-growing analytics and AI company helping financial and technology clients turn raw operational data into predictive insights. We combine domain expertise, modern data infrastructure, and applied machine learning to solve high-value business problems in credit, collections, risk, and customer engagement.

Were looking for a Data Scientist who thrives on execution someone who can own end-to-end analytics assignments, from data extraction to deployment, and deliver measurable impact to business outcomes.

Responsibilities:

• As a Data Scientist at Hudson Data, you will build, validate, and deploy data-driven models and analytical solutions that directly improve operational KPIs such as repayment rates, lead conversion, risk segmentation, and campaign efficiency.

• Youll work closely with cross-functional teams in product, engineering, and operations to turn analytical insights into scalable, production-ready tools.Design and execute end-to-end data science projects from problem framing to deployment.

• Collect, clean, and integrate data from multiple systems (SQL, Excel, APIs, Snowflake, AWS).

• Build and optimize predictive models

• Validate and monitor model performance on live data; retrain as needed.

• Automate recurring analytical workflows using Python, SQL, and other data pipelines.

• Present actionable insights through dashboards and concise summaries for business teams.

• Partner with engineering to productionize models via APIs or embedded scoring scripts.

• Document datasets, code, and methodologies for transparency and reproducibility.
  

Preferred candidate profile

• 5 to 7 years of hands-on experience in applied data science or analytics.

• Strong programming skills in Python (pandas, scikit-learn, NumPy) and SQL.

• Proven experience with predictive modeling, regression, classification, or clustering.

• Familiarity with cloud platforms (AWS / GCP / Azure) and version control (Git).

• Strong understanding of feature engineering, data validation, and model evaluation.

• Excellent communication skills and ability to translate analytical output into business impact.