Finance Data Scientist

Genesys empowers organizations of all sizes to improve loyalty and business outcomes by creating the best experiences for their customers and employees. Through Genesys Cloud, the AI-powered Experience Orchestration platform, organizations can accelerate growth by delivering empathetic, personalized experiences at scale to drive customer loyalty, workforce engagement, efficiency and operational improvements.

We employ more than 6,000 people across the globe who embrace empathy and cultivate collaboration to succeed. And, while we offer great benefits and perks like larger tech companies, our employees have the independence to make a larger impact on the company and take ownership of their work. Join the team and create the future of customer experience together.

Summary of Position:

We are seeking a highly skilled Data Scientist to drive data-driven decision-making across the organization by applying advanced machine learning and statistical techniques. This role is pivotal in solving complex business challenges, driving company strategy, and mentoring team members on advanced data science practices. You will work at the intersection of data science, finance, and business operations, building models that uncover hidden insights in customer behavior, help to optimize revenue streams, improve customer experience, and enhance operational efficiency.

A core focus of the role will be productionizing and deploying scalable models across different business functions, ensuring seamless integration into workflows. The ideal candidate will possess strong expertise in model interpretation, performance evaluation, and stakeholder communication, making machine learning outcomes understandable and actionable for non-technical audiences. This position will require collaboration with cross-functional teams to ensure the models continuously adapt to business needs and deliver measurable results.

You’ll play a hands-on role in all stages of the data science lifecycle—from data preparation, feature engineering, and model development to deployment, monitoring, and iterative improvement. A passion for ideation, problem-solving, and leveraging emerging technologies (e.g., GPTs, LLMs) will set you apart.

Location: US Remote (not limited to the states that the job is tagged to)

Key Responsibilities:

Model Development, Interpretation, and Evaluation

• Collaborate with data scientists and engineers to develop classification and regression models, ensuring predictions are accurate, explainable, and actionable for stakeholders.
  • Apply interpretability techniques to make model behavior transparent and build trust with non-technical teams.
  • Assess and monitor model performance using a range of metrics (e.g., accuracy, F1-score, AUC-ROC, mean squared error, R²) and identify areas for improvement.
• Experience with decision trees, ensemble methods, and LSTM models is a plus.

Feature Engineering and Data Exploration

• Develop, refine, and test new features to improve model performance and predictive accuracy.
  • Experiment with innovative ways to engineer variables and design multi-dimensional models to capture complex patterns and behaviors.
• Prepare, clean, and transform datasets, ensuring high data quality and consistency for model input.

Productionization and Model Deployment

• Deploy machine learning models into production environments, ensuring they are scalable, efficient, and maintainable.

• Collaborate with internal teams to integrate models into business processes and automate workflows.
• Monitor deployed models for drift, accuracy degradation, and performance bottlenecks, and apply retraining strategies as needed.

• Utilize CI/CD pipelines and MLOps best practices to streamline deployment and maintenance.

Data Visualization and Storytelling

• Create interactive dashboards and visualizations to communicate insights effectively.
• Translate complex machine learning outputs into compelling narratives that align with business goals and engage non-technical stakeholders.

Insight Generation and Business Impact

• Detect trends, anomalies, and patterns to uncover actionable insights that guide strategic business decisions.
• Measure the impact of data science initiatives on key business outcomes and continuously improve models to align with evolving objectives.

Stakeholder Collaboration and Communication