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Global Analytics Modelling Lead - Experian

196213 Requisition #
Thanks for your interest in the Global Analytics Modelling Lead - Experian position. Unfortunately this position has been closed but you can search our 525 open jobs by clicking here.



Strong hands on advanced analytics experience first and foremost, with demonstrable track record of driving complex data science projects with exposure to financial services and other relevant industries.


Exposure to and domain knowledge of big data architecture, engineering and data science platforms and machine learning/deep learning models with sound understanding of topics like model explainability, governance, maintenance and exposure to other emerging AI general application fields and topics


Significant demonstrable project experience on unstructured data analytics including exposure to real time decisioning products and associated workflows


Extensive experience building, training and validating machine learning models of all levels of sophistication in Python and/or R, with a strong knowledge of both industry standard and emerging open source packages and tooling.

Sector background likely to be from global financial services / fintech, data or software markets.



Driving Results:

 -Works to achieve goals, related to migration and innovation of products and driving growth, while overcoming obstacles and/or planning for contingencies.
 -Shows strong passion and strong sense of urgency about reaching targets.
 -Checks work of self and others on his/her team against required Experian quality standards.
 -Reviews performance and progress and guides on a regular basis to ensure Product Manager’s team is achieving results.
 -Tests to see if goals are sufficiently challenging and implements corrective action to fix missed performance goals.

Collaborating, Influencing & relationship management:

 -Invites and uses the opinions and perspectives of others
 -Engages people in a dialogue to gain commitment and bring them "on board", linking their perspective to the intent.
 -Checks with both sides of a discussion to ensure a common understanding
 -Adapts own approach to the audience, anticipating impact of words and actions, preparing for possible resistance and responding in an appropriate style, using a range of influencing styles   - both push and pull
 -Takes initiative to nurture and maintain strong internal networks / relationships / contacts.

Matrix Based Leadership:

 -Highly collaborative leadership style and can naturally deploy into planning, decision making and problem solving approaches
 -Engages stakeholders and his/her team in discussion around the business direction, conveying the organisational vision and direction and how the product, change and/or services fit in.
 -Possesses the necessary strength, confidence, competence & vision to project their leadership across the enterprise,
 -Is capable of winning “hearts and minds” and creating a sense of follow-ship across the Analytics community



-Methodical: An intuitive, flexible, hands-on executive with the ability to rapidly understand problem sources, backed by a process methodology oriented reengineering mind-set.

-Communication Excellence: A charismatic, thoughtful, personable communication style, both written and verbal, that people seek out for clarity.

-Regulatory Adherence: Champion a culture where the fair treatment of customers is at the heart of the Experian business. Ensure that by leading by example, you adhere to all regulatory requirements and apply appropriate controls in the interests of customers. Through the adoption of a top down approach, demonstrate a culture where all of our people understand their regulatory obligations, including what the fair treatment of customer’s means to them and our organization.

-Strategic: Able to clearly communicate internally and externally the strategy and the vision of the product line she/he is accountable for and how this will incorporate with the rest of the DA product suite and the broader Experian offering.

-Educated to degree level or equivalent
-Bachelor’s Degree in a numerical subject is most likely
-Masters level qualification or higher is preferred but not essential

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