Data Scientist role in Columbus, OH- Hybrid

Position: Data Scientist

Location: Hybrid Columbus OH (Need Local Candidates)

Duration: 6 months C2H

Need Senior Candidates.

Priority on Banking Domain Candidates.

About the Role

Client is seeking a skilled Data Scientist to join its analytics team. The ideal candidate will drive data-driven decision-making across the organization by developing predictive models, delivering actionable insights, and supporting key business initiatives across risk, fraud, operations, customer analytics, and digital banking.

Key Responsibilities

• Develop predictive, prescriptive, and machine learning models to support business initiatives such as risk assessment, fraud detection, credit analytics, and customer insights.
• Collect, clean, and transform large datasets from various banking platforms and systems.
• Perform exploratory data analysis (EDA) to identify patterns, trends, and opportunities.
• Build statistical and machine learning models using Python, R, or similar tools.
• Partner with business stakeholders to understand requirements and translate them into analytical solutions.
• Communicate complex findings in clear, actionable terms to both technical and non-technical teams.
• Deploy models into production environments in partnership with engineering teams.
• Monitor and validate model performance to ensure accuracy, compliance, and stability.
• Utilize SQL to query enterprise databases such as Teradata, Snowflake, or similar platforms.
• Adhere to banking compliance, data governance, and model risk management (MRM) standards.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• 3 7 years of professional experience in data science, machine learning, or advanced analytics.
• Strong programming skills in Python (preferred) or R.
• Hands-on experience with machine learning libraries such as TensorFlow, PyTorch, XGBoost, etc.
• Proficient in SQL and experience working with relational databases.
• Strong understanding of statistical analysis, hypothesis testing, and data modeling techniques.
• Experience working with large datasets in enterprise environments.

Preferred Qualifications

• Experience within the banking, financial services, or fintech domain.
• Familiarity with credit risk models, fraud analytics, AML, or customer segmentation frameworks.
• Strong communication skills and the ability to simplify complex concepts.

Soft Skills

• Strong analytical mindset and problem-solving ability.
• Ability to work independently and collaboratively.
• Detail-oriented with strong organizational skills.
• Comfortable working in a fast-paced, highly regulated environment.

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