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What We'll Bring:
At TransUnion, we strive to build an environment where our associates are in the driver's seat of their professional development, while having access to help along the way. We encourage everyone to pursue passions and take ownership of their careers. With the support of colleagues and mentors, our associates are given the tools needed to get where they want to go. Regardless of job titles, our associates have the opportunity to learn new things and be a leader every day.

Come be a part of our team - you'll work with great people, pioneering products and cutting-edge technology.

What You'll Bring:

• 2+ years of practical experience working as a data scientist applying machine learning to real-world problems
• Great communication skills to articulate technical aspect of model development to internal and external customers
• Exposure to credit portfolio management, loss forecasting and risk management
• Excellent understanding of machine learning techniques and algorithms, i.e., Classification, Regression, Clustering, Feature Engineering, Decision Trees, Gradient Boosting etc.
• Experience with common data science toolkits, such as R, Python, Anaconda, TensorFlow, SQL, Scikit-learn, Keras
• Proven applied statistics skills, such as distributions, statistical testing, regression, regulation
• Logical, evidence-based problem solving and critical thinking skills

• Graduate-level degree preferred with concentration in a quantitative discipline such as statistics, mathematics, economics, operations research, computer science or aligned discipline
• Working familiarity with cloud computing environments
• Exposure to NLP algorithms

• Utilize statistical and machine learning techniques to create high-performing models that comply with regulatory and privacy requirements and address business objectives and client needs.
• Ability to research and develop new approaches, keeping up to date. Tests new statistical analysis methods, software and data sources for continual improvement of quantitative solutions.
• Process, cleanse, and verify the integrity of data used for analysis.
• Do ad-hoc analysis and presenting results clearly.
• Communicate results and ideas to key decision makers.
• Create clear and easy to understand documents for models.
• Present analysis ideas, progress reports and results to internal and external customers.
• Collaborate with Lead Data Scientist to develop technical/business approaches and new or enhanced technical tools. Participates in collaborations for implementation.