Background

In just the past couple of years there has been an explosion of interest in this space among discretionary investors. This change has been driven by the proliferation of businesses generating valuable exhaust data. While traditional quantitative funds can do little beyond trying to predict price movements directly, analysts specialising in fundamental research can use new data sources to go far beyond the kinds of insights provided by traditional quarterly reports.

For example, consider Chipotle. A quantitative fund would be trying to predict changes in stock price. A discretionary investor can look at foot traffic across Chipotle locations, average spend for new vs returning customers, changes in customer retention - and many other dimensions which are invisible to most other market participants and hence are uncorrelated with price movements even though they paint a very rich picture of how Chipotle is going to perform in the future.

There are two major pain points for investors looking to enrich their research with Big Data:

  • Data engineering
    The most valuable datasets are ones never previously used by your competitors - and that means that no one has previously done the hard work of figuring out how best to access, cleanse, normalise, and featurise that data.
     
  • Synthesis
    Value creation in this space comes down to bridging a deep understanding of the potential uses and limitations of available data with a keen awareness of key drivers of valuation for different companies.

If you have an interest in driving groundbreaking work with novel data sources - a list of opportunities with Augvest member firms is provided below.

Opportunities

Data engineering

Data engineers form the core of any alternative data team - they help ingest and process large quantities of unstructured data.

Sought after qualifications:

  • Extensive experience with Python and SQL
  • Experience building ETL pipelines in AWS
  • Experience with Spark / Scala
Data science

Data scientists' greatest challenge is finding optimal ways to track business performance through time given incomplete samples. Some firms also task their data scientists with building systematic trading strategies on top of alternative data.

Sought after qualifications:

  • Extensive experience with Python, R, and SQL
  • Extensive applied statistics experience
  • Experience balancing panels a plus
  • Familiarity with ML / AI a plus
Quandamental analyst

On top of generating their own research, individuals who possess both a deep understanding of fundamental research and strong coding skills help their fellows analysts make the most of alternative data and help set direction for the data team.

Sought after qualifications:

  • Extensive fundamental research experience
  • Extensive experience with Python and SQL
  • Prior experience with alternative data a plus
Visualisation expert

Implementing visual dashboards makes all of the work of a fund's statisticians and engineers available to everyone at the firm in real time.

Note: these are the most scarce roles in this space.

Sought after qualifications:

  • Extensive experience with Tableau and / or D3.js
  • Some experience with Python and SQL
  • Strong grasp of applied statistics

The most impressive candidates are ones who have gone above and beyond their academic studies and work experience by engaging in independent research and / or development work in their spare time.

How to apply

If you are interested in any of the above opportunities please email your resume to team@augvest.com. Please put the role you are applying for in the subject of your email.

If you have any questions, please contact us.