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 translates 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.
Academic Qualification: 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 modeling 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 are 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 modeling approaches: credit scoring models, cost-sensitive learning, ARIME, 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.