Data Scientist- Marketing and Customer Analytics

at  Invictus Capital

Company Description
Invictus Capital is a global leader in alternative investments and developed the first tokenized cryptocurrency index fund in 2017. Invictus has grown to a team of over 40 and has continued to pioneer in that space. We have developed a world-class real-time, 24/7 investment platform supported by over 15,000 investors from 150 countries. We also have a dedicated team of quantitative financial specialists working on automated trading strategies using advanced machine learning and other data techniques.
Job Description
We are looking for a data scientist or data analyst who will own the customer analytics area. The initial goal will be driving the marketing strategy using data from our production systems. The role is part of the data science team but will work closely with the marketing team.
To be successful in this role, you should have experience with Data Mining Tools, Google Analytics, and be comfortable with processing and analyzing marketing data and have statistical knowledge and experience. It is key that you have attention to detail because data cleaning and data processing is roughly 80% of any data science job. Finally, interest in Marketing and business strategy as well as the ability to interpret information effectively is essential.
Technical job description

You will be responsible for:

• Collecting, analysing and interpreting customer data originating from our retail investor platform, and analytic sources
• Setting up, owning and maintaining the companies’ data warehouse as the single source of truth for the organisation
• Some examples of deliverables would be:
• Determining timeline and customer journeys across multiple touch points from first point of attribution to last point of attribution
• Analysis of Investor data defining customer profiles and behaviors
• Analysis of loss of potential customers over the investment journey
• Deliver insights on consumer trends and behaviours in the market
• Building customer segments according to behaviours
• Dashboard views for sales and marketing

We are looking for a data scientist / data analyst with experience analyzing customer data from online retail platforms. You should have the following skills and experience:

• At least 2 years experience working with a programming language used for data analysis like (Python, fR)
• At least 2 years of experience with SQL and databases like postgreSQL
• Experience with pulling data from REST APIs and using data analysis packages like Python Pandas or R-based packages
• Experience with setting up data warehouses and/or combining datasets from different sources
• At least 2 years of experience using cloud-based systems and tools (GCP: Big query, data studio, cloudsql or their AWS counterparts)
• Experience working with the engineering / technical team to understand the data in the backend
• Advanced Excel knowledge is essential

Qualifications

You should have a relevant formal qualification or equivalent knowledge:

• BSc Eng in a relevant field (Electrical Engineering; Information Engineering/Systems; Network engineering; or similar)
• BSc Comp Sci with network or ops experience; etc

Additional Information

If you have experience / understanding of the following it will beneficial in the job application process

• Previous experience doing data science tasks for a marketing team
• Setting up a data warehouse combining data from different backend systems

• Collecting, analysing and interpreting customer data originating from our retail investor platform, and analytic sources
• Setting up, owning and maintaining the companies’ data warehouse as the single source of truth for the organisation
• Some examples of deliverables would be:
• Determining timeline and customer journeys across multiple touch points from first point of attribution to last point of attribution
• Analysis of Investor data defining customer profiles and behaviors
• Analysis of loss of potential customers over the investment journey
• Deliver insights on consumer trends and behaviours in the market
• Building customer segments according to behaviours
• Dashboard views for sales and marketin