Ultimately the responsibility of the Senior Data Scientist is to be a key contributor to the wider data team and generate data-driven insights that directly drive and improve the business.
Below are some examples of how this can be achieved:
• Develop a deep understanding of the business context and our data which contributes and translate to impactful, relevant outputs.
• Design models and analysis alongside associated business implementation in partnership with key stakeholders.
• Develop predictive models and analyses with a strong focus on reproducibility, accuracy and evaluation.
• Communicate analysis and drive improved decision making with the broader business in a straight-forward, clear manner
• Contribute to company-spanning frameworks and tools for measuring business, credit and sales performance and evaluating business changes and pilots
• Collaborate with senior business stakeholders on analytic methods, data-driven decision making
• Provide support and mentorship to junior team members
REQUIREMENTS & QUALIFICATION
Education: A degree in computer science, mathematics or related quantitative field / demonstrated self-directed learning.
Experience: 4+ years in a role with a similar focus, 2+ years of predictive modelling experience preferred.
Knowledge / Skills:
▪ Advanced experience building predictive and explanatory models that solve specific business problems.
▪ Advanced skills in Python or R, ideally Python.
▪ Strong ability to communicate technical details visually and in written form to broader stakeholders.
▪ Strong attention to detail and investigative nature to ensure error-free, high quality, reproducible analysis.
▪ Experience working with automated systems to produce model outcomes with ongoing monitoring.
▪ Experience with collaborative data and software development via git and you’re comfortable in the command line.
▪ Enjoy abstracting, generalizing and create efficient, scalable processes that benefit the wider team.
• Experience with one or more of the following types of modelling approaches: credit scoring models, cost-sensitive learning, ARIMA and LSTM forecasting.
▪ Experience with running operational experiments and evaluating causal inference.
• Experience with Microsoft Azure, similar cloud providers and tools also a plus
• Familiarity with agile data ops development processes, unit testing, source control, continuous integration, etc.
• Experience with data visualization tools for stakeholder communication.
Competitive package covering a monthly salary, performance bonus and medical benefits reflective of the candidate’s experience and skills.