Data Scientist

Forsyth Barnes • Full-time • Cincinnati, OH, US • $130k - $145k / year • 9h ago

About the Company

Our client is a well-established organization in the insurance and financial services sector, committed to leveraging advanced analytics, machine learning, and artificial intelligence to enhance decision-making, improve customer experience, and streamline operations. The company is expanding its Data Science and AI function to drive innovation across areas such as risk assessment, claims automation, and fraud detection.

The Role

We are seeking a Machine Learning / AI Data Scientist to join a growing analytics and innovation team. This role offers an exciting opportunity to design and deploy advanced ML and AI models that directly impact key business areas including underwriting, fraud prevention, and customer engagement. You’ll work collaboratively with engineers, analysts, and business stakeholders to develop scalable, data-driven solutions that deliver measurable value.

Key Responsibilities

Design, develop, and deploy machine learning models for risk modeling, fraud detection, and process optimization.
Build and maintain robust data pipelines and model monitoring systems.
Collaborate with cross-functional teams to identify, scope, and execute AI-driven initiatives.
Conduct exploratory data analysis and feature engineering across diverse datasets.
Apply NLP, computer vision, or deep learning techniques to real-world insurance and financial use cases.
Communicate insights, findings, and recommendations effectively to both technical and non-technical audiences.
Stay up to date with emerging tools, frameworks, and best practices in ML and AI.

Candidate Profile

5+ years of experience in data science, machine learning, or applied AI.
Proficiency in Python, SQL, and modern ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Proven experience building and deploying ML models in production environments.
Background in insurance, finance, or risk analytics is highly desirable.
Experience with large-scale data, cloud platforms (AWS, Azure, or GCP), and MLOps practices.
Strong analytical mindset with excellent problem-solving and communication skills.
Passionate about using AI to create meaningful business impact.