At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.
Job Description for Data Scientist
Objectives and Purpose
- The Lead Data Scientist is responsible for applying expertise and best practices in full-stack data science capabilities including advanced data analytics, statistical modeling (AI/ML), MLOps, data engineering, and data visualization to develop data-driven solutions to enable business insights. This individual partners closely with Business Unit Leaders to model complex problems, derive analytical conclusions, and identify opportunities for improvement.
- The Lead Data Scientist will:
- Apply strong expertise in artificial intelligence through use of machine learning, data mining, and information retrieval to design, prototype and build next generation advanced analytics engines and services.
- Translate processes and requirements into analytical solutions and metrics, that can contribute towards data-driven solutions and strategies for the business.
- Develop user friendly analytical models for the business to provide data driven actionable insights which would enable a more informed decision-making.
Your key responsibilities
Data Science
- Develop customer-centric solutions with recommended data model and business intelligence (BI) technologies.
- Create repeatable, interpretable, dynamic, and scalable statistical models that are seamlessly incorporated into analytic data products, ability to discover correlations between variables and generate predictions/forecasts of data-driven decisions.
- Extract, transform, and load data from one data source (e.g., Databricks) into a single, consistent data source for data modelling and workflow orchestration purposes (i.e., representations of data flows and relationships).
- Advocate and educate on the value of data-driven decision making with focus on “how and why” of solving problems.
- Oversee forecasting models that process and analyze large quantities of data to identify key trends and business insights.
- Review and refine data visualizations that are detailed, clear, multi-faceted, and user-focused to effectively communicate data insights throughout a narrative.
Relationship Building and Collaboration
- Collaborate with business partners to identify analytical improvement opportunities based on defined pain points, problem statements, scope, and analytics business case.
- Strategize with IT Development Teams to develop a standard process to collect, ingest, and deliver data along with proper data models.
- Lead team members in defining business requirements, facilitating workshops and/or prototyping sessions focused on enhancing analytics product functionality.
- Collaborate with internal and external partners to develop analytics that advance end-to-end Data Science solutions and practices.
- Coordinate with DevOps, Database Teams to ensure proper design of system databases and integration with enterprise applications.
- Design data visualization solutions, with Enterprise Data and Analytics Team, that synthesize complex data for data mining, discovery.
Skills and attributes for success
Technical/Functional Expertise
- Experience and understanding of current and emerging data, digital, and IT technologies (i.e., generative AI), as well as analytics processes and service models.
- Proficiency in Data Analysis and Visualization, analyzing and interpreting large datasets using AI and machine learning techniques.
- Understanding of AI concepts, algorithms, and machine learning models and the ability to apply AI technologies to solve business problems.
- Ability to leverage generative models to create synthetic data, simulate scenarios, or analyze outputs into actionable insights.
- Ability to identify actionable insights from data and provide recommendations.
- Strong business acumen with knowledge of the Pharmaceutical, Healthcare, or Life Sciences sector is a plus, but we also value perspective gained from other sectors.
Leadership
- Strategic mindset of thinking above the minor, tactical details and focusing on the long-term, strategic goals of the organization.
- Advocate of a culture of collaboration and psychological safety.
- Decision-making and Autonomy
- Play a lead role in decision-making processes by providing data-driven insights and solutions.
- Shift from manual decision-making to data-driven, strategic decision-making.
- Proven track record of applying critical thinking to resolve issues and overcome obstacles.
Interaction
- Proven track record of collaboration and developing strong working relationships with key stakeholders by building trust and being a true business partner.
- Lead analytical approaches, integrating work into applications and tools with data engineers, business leads, analysts, and developers.
- Demonstrated success in collaborating with different IT functions, contractors, and constituents to deliver technical solutions that meet Takeda technology standards and security measures.
- Ability to work alongside intelligent machines and humanize data and insights.
- Passion for teaming, coaching, and learning with a growing team of Data Scientists.
Innovation
- Passion for re-imagining new solutions, processes, and end-user experience by leveraging advanced technologies (i.e., generative AI/ML), effective statistical models, and enterprise analytics platforms and tooling to support BI solutions and drive business results
- Advocate of leveraging intelligent machine learning/AI to effectively work alongside technology, humanize data and insights, and mature business capabilities
- Advocate of a culture of growth mindset, agility, and continuous improvement
Complexity
- High multicultural sensitivity to effectively lead teams
- Takes initiative to anticipate challenges and take proactive measures in addressing complex problems.
To qualify for the role, you must have the following:
Essential skillsets
- Bachelor’s degree in Data Science, Computer Science, Statistics, or related field
- At least 3-5 years of experience of data mining/data analysis methods and tools, building and implementing models, and creating/running simulations
- Familiarity with AI libraries and frameworks
- Experience and proficiency in applied statistical modeling (e.g., clustering, segmentation, multivariate, regression, etc.
- Demonstrated understanding and experience using:
- Data Engineering Programming Languages (i.e., Python, Pyspark)
- Distributed Data Technologies (e.g., Spark, Hadoop, H20.ia, Cloud AI platforms)
- Data Visualization tools (e.g., Tableau, R Shiny, Plotly)
- Databricks/ETL
- Statistical Model Packages (MLib/SciKit-Learn, Statsmodels)
- GitHub
- Excel
- Creating new features by merging and transforming disparate internal & external data sets
- Strong organizational skills with the ability to manage multiple projects simultaneously and operate as a leading member across globally distributed teams to deliver high-quality services and solutions
- Processes proficiency in code programming languages (e.g., SQL, Python, Pyspark, AWS services) to design, maintain, and optimize data architecture/pipelines that fit business goals
- Excellent written and verbal communication skills, including storytelling and interacting effectively with multifunctional teams and other strategic partners
- Demonstrated knowledge of relevant industry trends and standards
- Strong problem solving and troubleshooting skills
- Ability to work in a fast-paced environment and adapt to changing business priorities
Desired skillsets
- Degree in Data Science, Computer Science, Statistics, or related field
- Advanced experience in developing and applying predictive modelling, deep-learning, or other machine learning techniques
- Demonstrated understanding and experience in IICS/DMS (Data migration service)
- Experience in a global working environment
- Experience in solution delivery using common methodologies, especially SAFe Agile but also Waterfall, Iterative, etc.
Travel requirements
- Access to transportation to attend meetings
- Ability to fly to meetings regionally and globally
EY | Building a better working world
EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.
Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.
Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.
