Manager - Data Science - Bank

Key Accountabilities

- Collaborate on short (typically a few weeks) research projects for publication on research platform

- Onboard new data sets and write software to make them usable

- Inform analysis designs, especially with regard to causal and statistical inference

- Understand and apply your understanding of selection bias in alternative data sets

- Apply ML methods tactically, improving research deliverables without slowing down the research process

- Ideate and execute novel methods for longer term projects (typically a few months) with high novelty and potential impact on financial research

Stakeholder Management and Leadership

Data scientists need strong interpersonal skills. They will work closely with global team members and analysts and will need to act professionally and communicate technical concepts clearly to both technical and non-technical audiences in both written and oral communications.

They should share a teamwork-focused mindset, and be eager to contribute to research design conversations while also listening well to understand other researchers' viewpoints. They should approach conversations as peer researchers with a technical specialization rather than service providers. They should have some skill as finding the "problem behind the problem", and turn prescriptive requests for data or analysis into conversations about research goals, and then best execution.

Decision-making and Problem Solving

Good judgement during data analysis is a critical skill for this role. Data pre-processing often requires decisions that impact the quality and reliability of results, so data scientists should show knowledge of the impacts of their choices. Should you drop outliers? Or apply term-count thresholds during text analysis? These kinds questions are material for research quality, and at the same time are hard to evaluate in practice. For this reason, data scientists should have a good understanding of the extent of their own knowledge and should expect to have conversations about these trade-offs with colleagues.

Risk and Control Objective:

Ensure that all activities and duties are carried out in full compliance with regulatory requirements, Enterprise Wide Risk Management Framework and internal

Policies and Policy Standards

Person Specification :

- Experience with statistical inference, including the standard Python ML and data analysis stack (pandas, sklearn, tensorflow/keras/pytorch, numpy, scipy, statsmodels, etc.)

- An understanding of bias in observational data, including the basics of causal inference and selection bias adjustment

- Good judgement in data analysis and modeling that balances the need to deliver results with the sophistication of the analysis methods used

- Strong understanding of statistics, especially with handling dependent samples and time-series data.

- Strong writing skills

- Experience working with large-scale data analysis, especially using pyspark, on data sets with over 1B data points

- Experience using git for source control in a team environment, including reviewing pull requests

- Experience with a SQL dialect

Essential Skills/Basic Qualifications:

- Strong data analysis and ML skills

- A basic understanding of data pipelining and automation, with experience using PySpark on large data sets (over 1B data points) and SQL for data extraction.

- Strong understanding of the application of statistics to research design

- Strong communication skills, especially if evidenced by past writing (e.g. blog posts, articles, etc.)

Desirable skills/Preferred Qualifications:

- Strong skills with causal and statistical inference, including observational causal designs

- Past experience with large scale text analysis or geolocation data analysis

- Experience in quantitative finance