Lead Machine Learning Engineer

Company Description

Ready to make a difference? Experian has evolved into a global tech company and leader in data and analytics. We’re passionate about unlocking the power of data in order to transform lives and create opportunities for consumers, businesses and society. We’re a constituent of the FTSE 30 and for more than 125 years we’ve helped economies and communities flourish – and we’re not done.

Discover the Unexpected - Our 22k amazing employees in 30+ countries believe the possibilities for you, and the world, are growing. We’re investing in the future, through new technologies, talented people and innovation so we can help create a better tomorrow. To do this we employ ‘big-thinkers’ and ‘can-doers’ that share our purpose #uniquelyexperian

Job Description

Role Summary

Experian EITS is looking for an experienced machine learning engineer who will play a key role in bridging data science and engineering by implementing solutions to develop tools, standards and services that provide a global platform for machine learning (ML/AI) in the cloud. Experian has a long history of innovation in AI/ML across regions and business units. Recent inflections in areas such as Generative AI have increased the demand for faster evaluation and standardization of tools to provide an extensible platform for further innovation. We are looking for an experienced contributor with practical experience in ML modeling, data engineering and production ML platform architecture. The person in this role will work closely with other technology teams in order to shape the inception, implementation and deployment of new models.

Knowledge, Skills and Experience

Key Responsibilities

• Apply analysis, machine learning techniques, predictive modeling, and data mining to help solve business problems across global business units.
• Design and implement technical solutions for data ingestion, cleansing, transformation and storage
• Key contributor to productizing complex modeling/machine learning project, POCs and new product development, including design, coding and model deployment
• Problem solving with scientific rigor that results in precise and reproducible outcomes
• Exploration of novel and innovative technologies to enhance existing products and shape new products
• Work collaboratively with engineers and scientists across regions and business units

Qualifications

Qualifications

• Masters degree in a related quantitative field (Computer Science, Math, Statistics, Engineering, Physics, Economics)
• 10+ years of relevant working experience in a similar role, preferably involving business information, fraud, or credit data
• Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and statistical/mathematical programming languages (e.g. Python, R, SAS).
• Experience with Machine learning methods such as Neural Networks, Clustering, SVM, Ensemble models, Random Forest, and Gradient Boosting
• Excellent written, verbal, interpersonal communication, and presentation skills

Additional Information

Our uniqueness is that we truly celebrate yours. Experian's culture and people are key differentiators. We take our people agenda very seriously and focus on what truly matters; DEI, work/life balance, development, authenticity, engagement, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian’s strong people first approach is award winning; Great Place To Work™ in 24 countries, FORTUNE Best Companies to work and Glassdoor Best Places to Work (globally 4.4 Stars) to name a few. Check out Experian Life on social or our Careers Site to understand why.

Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is a critical part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.