Full Stack Data Scientist

Your work days are brighter here.

At Workday, it all began with a conversation over breakfast. When our founders met at a sunny California diner, they came up with an idea to revolutionize the enterprise software market. And when we began to rise, one thing that really set us apart was our culture. A culture which was driven by our value of putting our people first. And ever since, the happiness, development, and contribution of every Workmate is central to who we are. Our Workmates believe a healthy employee-centric, collaborative culture is the essential mix of ingredients for success in business. That’s why we look after our people, communities and the planet while still being profitable. Feel encouraged to shine, however that manifests: you don’t need to hide who you are. You can feel the energy and the passion, it's what makes us unique. Inspired to make a brighter work day for all and transform with us to the next stage of our growth journey? Bring your brightest version of you and have a brighter work day here.

About the Team

Ignite your passion for innovation and transform finance with Workday's Finance AI (FinAI) Innovation Center team.

Join our team of data science (DS) and machine learning (ML) experts where we do more than build algorithms. We empower finance professionals to achieve remarkable things. We automate mundane tasks, freeing them to focus on high-impact activities. We unlock hidden patterns and deliver actionable insights that lead to improved financial forecasting, optimized resource allocation, business process improvements, and significant cost reductions.

Work alongside passionate experts who share your drive to make a tangible difference. Develop your skills in a rapidly evolving field while bridging the gap between technical and non-technical partners, ensuring everyone understands the transformative power of AI.

This is more than just a job; it's an opportunity to be part of something truly special. Join us and become an architect of the future for finance.

About the Role

As our Full Stack Data Scientist (located in Pune, India), you'll participate in cross-functional data science efforts to understand business requirements and design, build, and implement innovative solutions that improve statistical modeling and machine learning.

Reporting to the Sr. Manager, Data Science, this role will evangelize machine learning, with a focus on identifying efficiencies, anomalies, risks, and gaps in business processes. The bulk of the work will involve data exploration, feature engineering, researching and building machine learning (ML) models and meaningfully collaborating with cross-functional business technology teams on model operationalization.

Finally, the Full Stack Data Scientist will act as an interface between Internal Audit, Finance, Business Technology and Product Teams.

Primary Responsibilities:

- Implement ML lifecycle from conceptualization to operationalization, including hypothesis generation, data exploration, feature engineering, model development, and results communication.
- Analyze and explore data to identify relationships, patterns, trends, risks, and opportunities.
- Formulate, build, test, and implement statistical and machine learning models to identify efficiencies, anomalies, non-compliance, and anomalous behavior in business transactions.
- Design and run experiments to validate hypotheses and improve model performance.
- Educate business teams on data science, AI, and machine learning principles and techniques.
- Evangelize data science and machine learning use cases by driving exploration, user engagements, consensus, and customer adoption.
- Prioritize tasks to improve productivity and ensure timely results.
- Collaborate with cross-functional teams to engineer workarounds and navigate project challenges.

About You

Basic Qualifications:

- 4+ years of hands-on experience in efficiently implementing machine learning projects. Preferably in the domains of anomaly and fraud detection, time series, statistical methods, experimental techniques, or similar.

Other Qualifications:

- Expertise in statistics and statistical concepts, including regression analysis, hypothesis testing, and statistical inference.
- Strong programming skills in Python, R, SQL and libraries such as pandas, numpy, sklearn, pyspark, or similar.
- Expertise in applied machine learning models and deep learning frameworks (Tensorflow, PyTorch or Keras).
- Proficiency in machine learning techniques such as dimensionality reduction, resampling, ensemble learning, anomaly detection, feature scaling and feature selection.
- Proficiency in data visualization tools such as Matplotlib, Seaborn, Tableau, Power BI, or similar.
- Expertise in evaluating models using visualization techniques such as confusion matrices, ROC curves, and precision-recall curves.
- Experience writing sophisticated SQL queries and ETL processes, including for data extraction, transformation, and loading