MidFull TimeENAsia/Pacific
Job Description
Responsibilities
As a Data Scientist II, you will collaborate and work across functional and multidisciplinary teams in a dynamic environment to develop an understanding of evolving/agile business needs.
Your expertise:
Synthesize complex statistical findings into clear, actionable insights for both technical stakeholders and executive leadership.
Perform hypothesis testing and A/B testing to validate assumptions and measure the impact of data-driven decisions.
Architect and refine high-signal features from raw data.
Leverage large language models (LLMs) to generate novel features and augment existing datasets.
Design, develop, and deploy advanced machine learning models (e.g., regression, classification, clustering) to address complex business problems.
Develop and maintain efficient data pipelines to extract, transform, and load (ETL) data from various sources.
Collaborate with software teams to ensure seamless integration of data science solutions into the broader technology stack.
Requirements and Qualifications:
BS/MS in Science (Statistics, Computer Science, Econometrics, Data Science, Artificial Intelligence ).
1-5 years of experience with data science or computer science fields
Experience with common data science toolkits, programming languages, visualization tools and SQL/NoSQL databases.
Good applied statistical knowledge with emphasis in business and finance related statistical distributions, statistical testing, modeling, regression analysis, etc.
Good foundation of computer science knowledge such as data structure, operating system
Familiar or prone to adopt design thinking methods.
Able to work under pressure and change, and balance among speed, reliability, interpretability.
Experience with code versioning, code review and documentation.
Effective communication skills
Experience with Optimization Tools like Gurobi, CPLEX, or Google OR-Tools.
A portfolio of side projects involving Chatbots, Semantic Search, or Target Labeling.
A passion for documentation and code versioning (we love a clean Git history).
Notes: We value "scrappiness" and the ability to pivot. If you’re someone who obsesses over the user experience as much as the loss function, you’ll fit right in.
