Senior Full Stack Data Scientist | Alabaster, AL

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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 Workday's Audit, Risk, and Intelligence Team (WARI) is committed to redefining finance functions through innovative data science (DS) and machine learning (ML) solutions.

We specialize in developing automation that demonstrates Workday's AI capabilities to streamline processes and uncover groundbreaking insights. Our solutions enable finance and internal audit professionals to reallocate their efforts towards high-value tasks, improve financial forecasts, optimize resource allocation, and reduce costs. Our data science solutions also play a crucial role in detecting and preventing fraudulent activities, safeguarding against financial losses. About the Role As our Sr. Full Stack

Data Scientist (located in Pune, India), you'll lead cross-functional data science efforts to understand business requirements and design, build, and implement innovative solutions that enhance 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. In addition, this role may serve as a domain authority on auditing machine algorithm development with a focus on ensuring trust, reliability, accuracy, and fairness of model results.

Finally, the Senior Full Stack Data Scientist will act as a crucial 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. Lead and project manage cross-functional efforts to operationalize ML-based solutions.

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. Promote risk and fraud prediction ML use cases to contribute to product development initiatives. 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 The ideal candidate is a professional who's more than a technical authority - you are a problem-solver, collaborator, and importantly a self-motivated leader. You have a curious and creative mind, always seeking new ways to approach and address sophisticated data problems. You are adaptable and flexible, able to work with different teams and technologies to deliver results.

You communicate clearly and effectively, sharing your findings and insights with others in a way that's understandable and practical. And you're passionate about your work, driven by a desire to use data to make a real impact in the world. Basic Qualifications: 7+ years of hands-on experience in efficiently implementing machine learning projects. Preferably in the domains of anomaly and fraud detection, 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, and SQL. Expertise in applied machine learning models and deep learning frameworks (Tensorflow, Py Torch 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 into a data lake. Ability to design and conduct experiments and evaluate model performance through cross-validation, hyper-parameter tuning, and similar techniques. Experience in Natural Language Processing using deep learning (RNN, CNN, LSTM, etc. ). Strong ability and willingness to lead ML operationalization engagements with diverse, multi-functional business and technology teams. Understanding of software development life cycle and artifacts required for different phases and stage gates.

Excellent project management and communication skills to present insights and recommendations to partners. Ability to explain sophisticated technical concepts to non-technical people. Experience with large-scale language models such as GPTs, BERT, or other LLMs is a strong plus. Experienced in applying concepts/philosophies for appropriate problem-solving and decision-making Proven track record to be highly collaborative Excellent interpersonal abilities, and communication skills Driven to make a positive difference with the team and with the company Our Approach to Flexible Work With Flex Work, we're combining the best of both worlds: in-person time and remote.

Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together.

Those in our remote " home office" roles also have the opportunity to come together in our offices for important moments that matter. Are you being referred to one of our roles? If so, ask your connection at Workday about our Employee Referral process!