Senior Data Scientist

Flexera saves customers billions of dollars in wasted technology spend. A pioneer in Hybrid ITAM and FinOps, Flexera provides award-winning, data-oriented SaaS solutions for technology value optimization (TVO), enabling IT, finance, procurement and cloud teams to gain deep insights into cost optimization, compliance and risks for each business service. Flexera One solutions are built on a set of definitive customer, supplier and industry data, powered by our Technology Intelligence Platform, that enables organizations to visualize their Enterprise Technology Blueprint™ in hybrid environments—from on-premises to SaaS to containers to cloud.

We’re transforming the software industry.  We’re Flexera.  With more than 50,000 customers across the world, we’re achieving that goal. But we know we can’t do any of that without our team.  Ready to help us re-imagine the industry during a time of substantial growth and ambitious plans?  Come and see why we’re consistently recognized by Gartner, Forrester and IDC as a category leader in the marketplace. Learn more at flexera.com

Job Summary:
We are seeking an accomplished and forward-thinking Senior Data Scientist to join our Data Intelligence team. The ideal candidate will drive the transformation of manual data curation processes into automated, AI/ML-powered workflows, ensuring higher efficiency and accuracy in how we manage and utilize data. This role involves collaborating with cross-functional teams—including product managers, operations, and data engineering—to implement innovative solutions that enhance data quality, reduce manual effort, and drive business value.

Key Responsibilities:

• Identify manual data curation and processing workflows, then design and implement AI/ML-driven automation solutions to improve efficiency.
• Lead the end-to-end development of ML solutions for data cleaning, validation, and standardization.
• Utilize statistical, machine learning, and deep learning techniques to solve complex data intelligence challenges.
• Perform exploratory data analysis to uncover insights, refine model features, and guide decision-making.
• Collaborate with data engineering teams to ensure robust and efficient data pipelines that feed into AI/ML systems.
• Research and implement state-of-the-art methodologies in ML, deep learning, and related fields.
• Oversee iterative experimentation, including A/B testing and model optimization.
• Mentor junior data scientists, reviewing code, models, and workflows for best practices.
• Communicate findings effectively to both technical and non-technical audiences.
• Champion AI/ML initiatives across the organization and identify new areas for innovation.

Required Skills and Qualifications:

• 5+ years of hands-on experience in Data Science or Machine Learning roles, with a strong focus on automating data-centric processes.
• Proficiency in Python or R and core data science libraries (e.g., NumPy, Pandas, scikit-learn).
• Expertise in at least one deep learning framework (e.g., TensorFlow, PyTorch, Keras).
• Strong foundation in statistics, mathematics, or related quantitative fields.
• Proven experience deploying ML models or automation solutions in cloud environments (AWS, Azure, or GCP).
• Demonstrated success in reducing manual workload through ML-driven data curation or process optimization.
• Familiarity with version control (Git) and CI/CD workflows in a data science context.
• Exceptional problem-solving and analytical skills.
• Effective communication skills for presenting complex insights to diverse stakeholders.

Preferred Qualifications:

• Experience with large-scale data processing frameworks (e.g., Spark) and distributed computing.
• Familiarity with specialized domains like Natural Language Processing or Computer Vision.
• Knowledge of MLOps tools (MLflow, Databricks, Kubeflow) and best practices for continuous model delivery.
• Background in experimentation methodologies, A/B testing, and performance evaluation.
• Experience in data governance, data lineage, or data stewardship best practices.
• Publications in AI/ML conferences or journals.