Role overview
We are a diverse team that takes pride in understanding the perspectives of others. We fully embrace working remotely and we are eager to act, improve and accelerate progress inside and outside of our organization.
To drive revolutionary changes in society and make crypto useful, we delight our customers with world-class products, deep care, and intentional empathy.
As a Senior Data Scientist (Finance), your objective is to translate financial concepts into machine learning solutions that drive strategic decision-making and operational efficiency within the finance department. This includes forecasting the topline, key parameters for financial planning, and providing sound scenario analysis and stress testing. You'll lead these critical data science initiatives throughout the project life cycle, from initial requirements gathering and stakeholder alignment to model creation, deployment, and ongoing performance monitoring and refinement. Your expertise will be crucial for Bitsoâs Financial planning outcomes.
What you'll work on
- Have a degree in a quantitative field such as actuarial science, finance, mathematics, data science, or economics.
- 3+ years of experience as a data scientist or an advanced degree (Masters or Ph.D.) in Mathematics, Finance, Risk management, Data Science, or Machine Learning and 2+ years of industry experience.
- Experience with classic time series forecasting. Including ARIMAs, GARCH, and more advanced techniques.
- Understanding of simulation techniques like monte carlo and markov chain.
- Experience in both supervised and unsupervised modeling.
- Strong proficiency in SQL.
- Strong proficiency in R or Python.
- Understanding of software engineering best practices, including version control, code reviews and automatic testing.
- Strong communication skills and the ability to convey complex technical concepts to both technical and non-technical stakeholders.
What we're looking for
- Understanding of advanced quantitative finance techniques such as hidden markov models, deep learning for time series, state-space models, extreme value theory and scenario analysis.
- Experience working with a FP&A area.
- Understanding of big data manipulation tools such as Spark or Koalas.
- MLOps best practices experience.
- Databricks experience.