Business Data Scientist, Machine Learning Finance
Location
Bangalore | India
Job description
Minimum qualifications:
- Bachelor's degree in a quantitative discipline or equivalent practical experience.
- 6 years of industry experience, with 4 years in machine learning, statistical modeling, statistical software (e.g., Python, R), and database languages (e.g., SQL).
- Experience working in a cross-functional setting, managing the full lifecycle of projects, and coaching members in managing their own projects.
Preferred qualifications:
- Master's degree or PhD in a quantitative discipline (e.g., Statistics, Computer Science, Math, Engineering) or equivalent practical experience.
- Experience with both supervised and unsupervised learning (e.g., time series forecasting, clustering, classification) or Big Data techniques (e.g., Spark, Pig, Hive).
- Experience with ambiguity, prioritizing needs, and delivering results in a dynamic environment.
- Excellent written and verbal communication skills to translate technical solutions and methodologies to senior leadership.
About the job
As a Quantitative Analyst, you will be responsible for analyzing large data sets and building expert systems that improve our understanding of the Web and improve the performance of our products. This effort includes performing complex statistical analysis on non-routine problems and working with engineers to embed models into production systems. You will manage fast changing business priorities and interface with product managers and engineers.
As a Business Data Scientist, you will work on business challenges across multiple business areas through the lens of business generation. You will collaborate with data engineers, analysts, and product managers to create data solutions to enable finance partners to make informed decisions, manage risks, and opportunities.
Responsibilities
- Partner with Finance leadership and their teams to understand business context and improve on data insights and solutions to deliver actionable information to the business.
- Apply machine learning methods to solve problems including the full modeling lifecycle (e.g., data manipulation, building and evaluating models, interpreting and communicating results, transitioning solutions to production, performance monitoring, recalibrating models, etc.).
- Create data visualization to summarize the modeling insights and communicate them to internal team members and business stakeholders.
- Grow data science and engineering skill sets.
- Coach members, share knowledge, and support the continuous development of tooling within the broader team.
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