Open Position: Chief Data Scientist
The Firm seeks an experienced leader to lead the development, implementation, and management of a data science platform to support the creation of innovative investment strategies across asset classes and risk factors.
This individual will be responsible for the following activities:
· Hire and manage a new data science team
· Establish a investment-based research agenda (in conjunction with Investment team) that rigorously explores how new data and new computational methods could be used to identify investable signals within the commercial context of the firm.
· Identify and test possible signals and create working interim solutions starting with a minimum viable solution and iteratively refining until a satisfactory solution is achieved
· Develop idea lab and a solution testing environment/methodology with Investment team (i.e., backtesting methodology)
· Lead the design, implementation, and operation of a state-of-the-art “big data” analytics platform that is scalable and innovative in the way it extracts, manages and analyzes data to be used to develop investment strategies
· Establish data policies, standards, organization and data governance
· Develop innovative approaches to linking internal systems with external data
- Makes strategic recommendations on data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
- Analyze data, draw insights, and prepare reports in a cohesive, intuitive, and simplistic manner to stakeholders
- Have a seat at the management table and help plot the company’s course
- FYI: Rosetta has a firm No – AH policy
The candidate will possess the following characteristics:
· Strong commercial orientation and the proven ability to use analytics to drive commercial results
· Ability to independently deliver results
· Strong management skills, particularly in situations with remote personnel
· Ability to draft formal specifications and enable data science team to elevate strategically targeted prototypes to production
· Ability to build and manage data science team and evaluate and manage outsourcing opportunities
· Ability to communicate clearly with all internal associates, including research, trading, management, development, and operations personnel, and external stakeholders
· Demonstrated interest in understanding the broader business context within which the technology solution is needed (i.e., focus on the problem, not the code)
· Ability to pivot between prototyping, refactoring, and production-ready development styles, ideally demonstrated by prior experience
· Have the foresight and business context to provide the optimal data science vision, strategy, and best practices for the company
· Ability to collaborate with stakeholders, partners, and clients to ensure all parties are aligned and supportive of the project's priority, requirements, timeline, and objectives.
· Entrepreneurial mindset: creative, focused, GSD, Be the Pig Attitude
• Graduate degree in computer science, machine learning, or related technical field
• At least five years of relevant experience in a data science/analytics role, preferably in an entrepreneurial environment
• Significant industry experience working with machine learning frameworks, large-scale data processing systems, or cloud infrastructure
• Significant industry experience developing enterprise or data products and nontraditional datasets.
• Experience in recruiting, managing, training, and retaining a team of ambitious and highly skilled data scientists.
• Experienced leader who has had accountability and authority over a company's data science resources.
• Experience in applying data analysis techniques to a large datasets using infrastructure such as Hadoop, Spark, MongoDB, or other Mapreduce Software.
• Substantial experience with programming scripts such as Python, Java, Scala, C++ in Linux/Unix, Storm, Julia, SQL, Matlab, Mahout and R. Fluency in My SQL, Oracle SQL, or Postgres.
• Knowledge of univariate and multivariate statistical analyses. Knowledge of statistical model building using both frequentist and Bayesian approaches.
• Experience building predictive models using machine-learning techniques such as decision trees, random forests, gradient boosting machines, support vector machines, regularization, etc.
• Experience with web services such as AWS, DigitalOcean, Redshift, S3, and Spark and capability to connect data using web API, REST API, and web crawling techniques.
• Experience in creating and implementing machine learning algorithms and advanced statistics such as: regression, clustering, decision trees, exploratory data analysis methodology, simulation, scenario analysis, modeling, and neural networks.
• Experience in visualizing data to stakeholders in a simple and concise manner through visualization software such as ggplot, D3,Tableau Qlinkview, Periscope, Business Objects, or other similar software.
• Experience in analyzing data from business line data applications and data providers such as Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, Nielsen, Comscore, Simmons, MRI and etc...