Senior Data Scientist, Innovation Lab

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Job Description

Experian Innovation Lab is a research and development unit at Experian formed with the desire to work in collaboration with Experian's departments to enhance relationships with clients and acquire strategic datasets. Experian® is a global leader in providing information, analytical tools and marketing services to organizations and consumers to help manage the risk and reward of commercial and financial decisions. Using our comprehensive understanding of individuals, markets and economies, we help organizations develop customer relationships to make their businesses more profitable.

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 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