Head of Data Science
As our Head of Data Science, you will be essential to automation and product innovation by combining the power of our vast gamut of internal data assets with 3rd party structured and non-traditional sources of data. You will build the advanced analytics and business intelligence strategy and roadmap and collaborate with technology and data teams to establish the enabling technologies. You will be instrumental in creating new business value by leveraging data across S&P Global and within S&P Global Ratings to extend our capabilities in data science, artificial intelligence, and machine learning. You will leverage data science and business intelligence to meet the needs of S&P Global Ratings and our clients by developing new products to meet their evolving needs. At S&P Global Ratings, data is at the center of everything we do, and we are the leader in the world of credit risk decision-making. Responsibilities:
You will be expected to bring your machine learning, NLP, NLG, statistical analysis, programming and scripting capabilities, combined with exceptional communication skills. The role will advance our data science and business intelligence capabilities, broaden exposure to analytical toolkits and alternative data assets within the S&P Global Ratings organization and be an active contributor to the S&P Global data scientist community. Hands-on Leadership Capabilities:
Building a strong Data Science team:
- Drive, collaborate and align with internal stakeholders and our clients to ideate and build solutions in the next generation analytics space.
- Lead a bold agenda around the use of our data assets, both in the traditional and alternative data domains, in new creative ways.
- Promote and teach the use of big data and machine learning tools.
- Engage with various technology and data teams to design the optimal data architecture for a new scale platform and infrastructure to support data and advanced analytics.
Executing on our Priorities:
- Build, manage, inspire, and coach a high impact data science team to develop quantitative models (predictive, sentiment etc.) using NLP, NLG, machine learning, or data mining techniques to create business value.
- Build a culture of innovation and focus the team to be product-driven
Experience & Qualifications:
- Drive value to the broader business. Develop and communicate goals, strategies, tactics, project plans, timelines, and key performance metrics to align with multiple internal stakeholder departments
- Utilize your knowledge of advanced technologies to develop commercially viable product ideas, to maximize growth, customer value, and internal value for our analysts.
- Deliver data science solutions, including managing and prioritizing data science and business intelligence requests and turning them into clearly defined projects for the team, with well-defined parameters and deliverables.
- Build strong and effective relationships with key stakeholders, and serve as strategic advisor to multiple functional areas across the organization. Builds rapport with senior leaders to achieve departmental objectives.
- Strong leader of people and change agent to guide transformation from a data and content organization to a high performing, customer-focused content organization functioning as one team.
- Act as member of the Data and Content Organization Leadership team, working closely with stakeholders across Ratings and the divisions, integrating Ratings into the S&P Global Data strategy, including use of best practices, capabilities and global frameworks
- Place people and culture first in all that we do, using Lean Leadership and Management tools, build an empowered culture and inspiring workplace evidenced through employee engagement surveys and talent-led SMART goal setting
- Lead, mentor, and develop a high performing team of content leaders, with a shared growth mindset, covering all content areas across the company that can deliver content to customers (internal & external) effectively across the end-to-end pipeline
- Master's degree in a quantitative discipline, finance or equivalent experience preferred.
- Detailed knowledge (10+ years) in multiple data domains - familiarity with, and knowledge of financial data assets, as well as application of alternative data assets (such as geo-spatial data, social media, sensor signals, etc.) in advanced analytics.
- Expert theoretical knowledge of machine learning modeling techniques and advanced applied skills in developing predictive targeting models within distributed computing platforms such as Hadoop, AWS, Azure, GCP using tools like Spark, Scala, SAS, R, Python, Bayesia, H2O, Storm, Yarn, Kafka,
- Worked with analytics and visualization tools such as Alteryx, SPSS, Qlik, Tableau, Microstrategy etc. Must be well versed in languages such as SQL, Python, R.
- Proven track record evaluating ideas and developing tests/prototypes to prove out which are most likely to succeed.
- Prior experience developing and managing products, with proven track record for owing the end-to-end "business development life cycle" for products, taking them successfully from concept to launch and continuous improvement
- Strong understanding of machine learning, statistical and probability analysis, predictive modeling and hypothesis testing.
- Thought leader of data science. Must be skilled at representing S&P Global Ratings in external forums (conferences, industry meetings) as well as at promoting advanced analytics to internal constituents across the enterprise. Must possess cross-cultural sensitivity and be able to work in an international team including managing offshore and outsource resources.
- Ability to translate strategic imperatives at the macro level into specific initiatives and priorities for operational creation and execution.
- Must have good operational skills as well as strategic understanding of business and product requirements.
- Knowledge of financial engineering and quantitative modeling operations, preparation of budgetary plans, operational plans, forecasting activities.
- Thorough knowledge of technology systems; has the ability to work with development teams to ensure on-time, on-budget delivery of effective database technology.
- A thorough understanding of applied statistics including sampling approaches, time series analysis and data mining techniques
- Continuously monitor and keep abreast of tools, methodologies, data, and competitive movement and respond with strategies and tactics to maintain or alter strategy.
Grade: 14 (internal purpose only)
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