Senior Data Scientist, ML

About Nclusion

About the Role

What You’ll Do

• Build ML Models – Develop models for credit risk assessment, fraud detection, and customer segmentation
• Data Analysis – Extract meaningful insights from complex financial datasets, create robust feature sets, and develop innovative approaches to evaluate creditworthiness
• Risk Assessment Systems – Design and implement automated risk scoring systems
• Fraud Detection – Create and maintain ML models to identify potential fraud patterns
• Model Monitoring and Optimization – Continuously monitor model performance, identify bias, and optimize algorithms
• Cross-functional Collaboration – Work closely with engineering, product, finance, and compliance teams to implement ML solutions that meet regulatory requirements and business need
• Data Visualization & Reporting – Design and maintain comprehensive dashboards and reports to communicate key insights to stakeholders

What You Bring to the Table

• 8+ years of experience working with machine learning, data science, and analytics in production environments
• Extensive experience with Python libraries for data analysis and deep learning frameworks
• Proven track record implementing supervised and unsupervised learning algorithms and deep learning models
• Strong foundation in statistical analysis, experimental design, and data visualization
• Experience with big data processing frameworks and databases (BigQuery, Airflow, Spark, PosgreSQL)
• Understanding of financial services, credit risk assessment, and fraud detection
• Ability to explain complex technical concepts to non-technical stakeholders and collaborate effectively across teams
• A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience)

• 📈 401k with a match!
• 🩺 Medical Insurance
• 🦷 Dental Insurance
• 👓 Vision Insurance
• 💸 Competitive compensation & equity – We believe in sharing success.
• ✈️ Flexible PTO – We focus on impact, not tracking vacation days. We encourage a minimum of 14 days.
• 🍽️ In-office lunch, team events & culture