Senior Data Scientist

  • Competitive
  • London, England, United Kingdom
  • Permanent, Full time
  • EY.
  • 18 Apr 19

Senior Data Scientist

Technology has always been at the heart of what we do and deliver at EY. We need technology to keep an organization the size of ours working efficiently. We have 250,000 people in more than 140 countries, all of whom rely on secure technology to be able to do their job every single day. Everything from the laptops we use, to the ability to work remotely on our mobile devices and connecting our people and our clients, to enabling hundreds of internal tools and external solutions delivered to our clients. Technology solutions are integrated in the client services we deliver and is key to us being more innovative as an organization.

EY Technology supports our technology needs through three business units:

Client Technology (CT) - focuses on developing new technology services for our clients. It enables EY to identify new technology-based opportunities faster, and pursue those opportunities more rapidly.

Enterprise Workplace Technology (EWT) - EWT supports our Core Business Services functions and will deliver fit-for-purpose technology infrastructure at the cheapest possible cost for quality services. EWT will also support our internal technology needs by focusing on a better user experience.

Information Security (Info Sec) - Info Sec prevents, detects, responds and mitigates cyber-risk, protecting EY and client data, and our information management systems.

Function: Data Office
Sub Function: Organization Intelligence, Data Ecosystem
Job Rank: Assistant Director
Scope: Global

Job Summary: Senior Data Scientist, Organization Intelligence

As Senior Data Scientist (PDS) you will become a key member of Ernst & Young's Global Data Office (GDO). Within the Data Ecosystem (DE) team you will be a key member of the team responsible for imagining, designing, developing and delivering data and software assets which incorporate data, models and analytic methods.

In addition to colleagues within the Data EcoSystem team, this will require close collaboration with Market Activation Pillar, Commercial and Risk Management, each of EY's Service Lines, Regions, IT Services and other key stakeholder groups with EY.

Background to the development of the development of the EY Data Office and the data ecosystem

EY's data strategy is built on the following principles:

  1. EY must maintain a high degree of trust with our clients data across both Inputs and Outputs:
    • Inputs (Sourcing): Data supplied by EY clients will be rationalized into information value chains that are treated in accordance with legal, regulatory, contractual and professional standards.
    • Outputs (Usage): Information value chains will be ethically leveraged to maximize value across a number of commercial patterns to enhance client services and create new market opportunities.
  2. All data available in the data ecosystem can be utilized across all service offerings and business models subject to usage and access restrictions
  3. Separate Supply and Demand: Supply (Ingestion) will largely be decoupled from Demand as supply is driven by availability of sources
  4. Data alliances are key to gain critical mass: It will be difficult for EY to rely entirely on collected and purchased data.
  5. Data will be managed by source system: Each source system will have a roadmap to meet demand requirements

Essential Functions of the Job:
  • As a data scientist, develop a firm understanding of the essential business requirements through interaction with, and interrogation of, business SMEs and translates that understanding into designs that addresses the requirements, with maximum generality, using data science methods, including:
    • Data assembly, cleansing, validation
    • Data visualization
    • Statistical modeling, supervised and unsupervised machine learning, mathematical programming
    • Deployment of algorithms
  • Work with the Organization Intelligence Leader and other stakeholders to confirm product functionality and develop a product lifecylce
  • Inspects data to confirm that it is consistent with expectations; modifies designs as necessary.
  • Using a data science toolkit and appropriate data, creates prototypes of key data manipulations, visualizations and mathematical modeling elements, validating and refining his/her designs.
  • Delivers algorithms. methodology and data to Data architecture teams building products
  • Validates the designs with the business SMEs examples, prototype demonstrations and documentation and iterates designs in response to negotiations with business SMEs.
  • Conveys the designs to the software development teams via discussion, documentation and prototype code. Ensures that the software implementation conforms to design expectations via inspection and testing.
  • Where appropriate collaborates with Market Activation team and Service Lines in the development of training and reference materials to ensure that solutions are utilized in the most effective and highest quality manner.
  • Manages and maintains the project execution processes: new project on-boarding, monitoring progress against scope and timeline objectives directing remediation where required, cost recovery.
  • Builds personal relationships with Sector/Service Line Data Office leaderships to increase awareness of the Data Science asset development competency,
  • Actively participates in communities of data scientists across the firm.
    Analytical/Decision Making Responsibilities:
  • Provide guidance to solution build teams so that Analytics assets and services are built and delivered in line with policies, processes, principles and enabled for reuse
  • Identify appropriate tools, techniques in ML and AI for solving critical business problems
  • Advise on the development of standard data quality management frameworks, processes, policies to support analytics
  • Design and support the development and maintenance of an end to end data lifecycle management approach




Knowledge and Skills Requirements:
  • Solid understanding of a range of statistical and machine learning techniques and algorithms, such as logistic regression, KNN, decision trees, SVM, CNN, etc.
  • Understand of text analytics and text matching algorithms
  • Expertise in applied statistics: distributions, statistical testing, business, etc.
  • Experience with common data science toolkits, such as Python, R, etc. High degree of proficiency in Python.
  • Familiarity with Graph technologies
  • Experience with data visualization tools, such as Spotfire, Tableau, etc.
  • Experience with Alteryx or other data wrangling tools
  • Good scripting and programming skills; SQL proficiency.
  • Sound written communication skills
  • Experience in architecting data science based systems and working with Engineering teams to facilitate deployment
  • Ability to effectively work across the GDO to gather requirements and gain consensus
  • Pragmatic approach to problem solving - ability to deliver 80% solutions in short term
  • Ability to take a broader perspective over multiple domains, identify commonalties to deliver commercially effective solutions

    Other desirable skills
  • Experience with NoSQL databases,
  • Experience with Cloud analytics architectures

Team Responsibilities:

At EY we believe that it is important for the Data Office to be a multi-disciplinary. This requires that you demonstrate your ability to influence others and expand your area of influence beyond "traditional data management". To satisfy these objectives, supervision responsibilities will include:
  • Effectively coordinate with a large number of internal stakeholders where there will be influence but not authority
  • Works independently under minimal guidance with regular alignment with Data Ecosystem leadership
  • Provides formal and informal coaching on-the-job to grow team members to their full potential.
  • Delivers results through others, leveraging the ability to lead through influence - performance measured on outcomes

Other Requirements:

Moderate travel, mainly to India, Western Europe and USA
Long hours may occasionally be required to meet project commitments and/or preparing materials for clients (internal and external). Overtime may be required as per country overtime policy
Flexibility in working hours to accommodate workload and multiple time zones, as needed

Job Requirements:

Education:
Masters or PhD in Analytics, Operational Research, computer science or similar discipline

Experience:
  • Experience in data analytics, management of third party data sources, application product development,
  • Engagement within a multi-cultural, multi-disciplined, globally dispersed team
  • Experience in the development of packaged data assets / products

Certification Requirements:

Any Data Science applicable Vendor or Industry certification is preferred not mandatory