Data Science @FanCodeWe have one mission, Create a platform for all sports fans. Built by sports fans for sports fans. We’re at the beginning of our story and growing at an incredible pace. At FanCode we are building one of a kind personalised experience for all sports fans. Using the massive amounts of sports engagement data we possess, we are developing cutting edge machine learning and deep learning solutions to improve user experience and provide value across business verticals. Given the scale at which FanCode operates, we employ cutting edge big data technologies in developing and maintaining the end to end ML/DL pipelines. Main areas of focus are user experience personalisation, product recommendation systems, user engagement analytics and forecasting.
Your Role:
At FanCode Data Science team we are looking for highly skilled and motivated Data Scientists who are passionate about innovating and developing machine learning and data analytics solutions to build on top of the massive sports data mine which we hold.
We provide the opportunity to be part of a young, energetic and dedicated team working to create tools, services and processes which help our product and organisation be more data driven and improve user experience.
The candidate will be working closely with product, business analytics and management teams to make data driven decisions.
The candidate will also work closely with data engineering, software development teams to build, deploy and maintain end to end machine learning pipelines in multiple environments.
Must Haves:
2 - 4 years experience in delivering and maintaining solutions for large scale structural data problems.
Deep experience in applied data analysis, including A/B testing and observational causal inference methods.
Strong communication skills to present opportunities, research and insights to key stakeholders of the team using data visualization tools like python, R, looker etc.
Good to Haves:
Love Sports.
Strong fundamentals in math behind basic statistical and machine learning models.
Proactivity in performing data exploration to understand user behavior and identify opportunities for improving customer experience and operation efficiency.
Prior experience is large scale personalisation and recommendation systems.
Prior experience in working with distributed computing systems like dask, spark etc.
Prior experience in predictive customer analytics models like Churn modeling, LTV modeling, Demand Forecasting, User segmentation etc.
