Role Description As a Senior Data Scientist at the Experian Innovation Lab, your role will be to research and develop analytical solutions, prototype new products, and evaluate data assets. You must bring an experience in predictive modeling, machine learning, and deep learning to this position. You will report into the Sr. Principal Data Scientist. You'll have the opportunity to: - Create advanced machine learning analytical solutions to extract insights from diverse structured and unstructured data sources. - Unearth data value by selecting and applying the right machine learning, deep learning and processing techniques. - Refine data manipulation and retrieval through the design of efficient data structures and storage solutions. - Innovate with tools designed for data processing and information retrieval. - Dissect and document vast datasets, analyzing them to highlight patterns and insights. - Solve complex challenges by developing impactful algorithms. - Ensure model excellence by validating performance scores and analyzing Return on investment and benefits. Qualifications - PhD degree in Machine Learning, Data Science, AI, Computer Science, or a related quantitative field. - 2+ years of experience in AI, data science, or predictive modeling. - Proficiency in multiple programming languages, including Python. - Experience in deep learning (CNN, RNN, LSTM, attention models), machine learning methodologies (SVM, GLM, boosting, random forest), graph models, or reinforcement learning. - Experience with open-source tools for deep learning and machine learning technology such as pytorch, Keras, tensorflow, scikit-learn, pandas. - Experience with large data analysis using pySpark. - Experience with LLMs and the relevant tools in the Generative AI domain. - Experience with Hadoop and NoSQL related technologies such as Map Reduce, Hive, HBase, mongoDB, Cassandra. - Experience modifying and applying advanced algorithms to address practical problems. - Experience with online, mobile marketing analytics. - Experience with GPU programming. Benefits - Great compensation package and bonus plan. - Core benefits including medical, dental, vision, and matching 401K. - Flexible work environment, ability to work remote, hybrid or in-office. - Flexible time off including volunteer time off, vacation, sick and 12-paid holidays.

• Lead a data science team and cross-functional projects • Innovate and improve machine learning models • Partner with business leaders and technical experts across the company • Develop new data sources and improve our modeling methodology • Apply models with sound risk management

• Partner directly with CX directors, managers, and executive leadership to deliver prescriptive, data-backed guidance for strategic planning. • Analyze customer health scores, product usage, and support trends to perform deep-dive retrospective analyses on churn and contraction drivers. • Design and execute impact measurement models that move past baseline usage metrics to quantify actual gross retention rate (GRR) improvements, net revenue retention (NRR) gains, and process efficiencies. • Lead the quantitative analysis for complex special projects, including annual account carves, territory balancing, and customer segmentation strategies. • Using Large Language Models (LLMs) to rapidly prototype scripts, automate ad-hoc reporting requests, and build lightweight data workflows. • Actively leverage the native AI components within Snowflake and our broader business intelligence stack to accelerate context gathering and surface predictive customer trends. • Map out how multi-source data flows between our primary platforms to create a unified, accurate view of the post-sales ecosystem. • Write high-performance, production-grade SQL queries directly within Snowflake to manipulate and model complex datasets. • Serve as part of the core CX liaison to the Enterprise Data & Analytics (ED&A) team, owning downstream data requirements and standardizing metric definitions.

• You will bring statistical rigor to business decisions. • Within the BI team, your mission is to go beyond descriptive analysis by applying statistical and machine learning models to find patterns, validate hypotheses, and generate forecasts that guide real strategies. • Day-to-day, you will model complex problems with Python, work with large volumes of data in ClickHouse and BigQuery, and collaborate with product and operations teams to ensure the models deliver real value. • Conduct statistical analyses to answer business questions: hypothesis testing, correlation analysis, segmentation, and predictive modeling; • Perform exploratory data analysis (EDA) to understand the data before modeling, identifying patterns and inconsistencies; • Write complex SQL queries in ClickHouse and BigQuery to extract and prepare data for analysis and modeling; • Use Python for data manipulation, exploratory analyses, and model prototyping; • Communicate results and insights clearly to business teams—translating statistics into decisions; • Collaborate with the BI team to define metrics, data quality standards, and analytical best practices; • Monitor models in production, tracking performance and proposing continuous improvements;