(Sr.) Data Scientist Taipei BTSE – Data / Full-time / On-site apply for this job About BTSE:
彼特思方舟 is a specialized service provider dedicated to delivering a full spectrum of front-office and back-office support solutions, each of which are tailored to the unique needs of global financial technology firms. 彼特思方舟 is engaged by BTSE Group to offer several key positions, enabling the delivery of cutting-edge technology and tailored solutions that meet the evolving demands of the fintech industry in a competitive global market.
BTSE Group is a leading global fintech and blockchain company that is committed to building innovative technology and infrastructure. BTSE empowers businesses and corporate clients with the advanced tools they need to excel in a rapidly evolving and competitive market. BTSE has pioneered numerous trading technologies that have been been widely adopted across the industry, setting new benchmarks for innovation, performance, and security in fintech. BTSE’s diverse business lines serve both retail (B2C) customers and institutional (B2B) clients, enabling them to launch, operate, and scale fintech businesses. BTSE is seeking ambitious, motivated professionals to join our B2C and B2B teams.
We are hiring a Senior Data Scientist / Analytics Engineer to be a senior individual contributor on the BI team. You will be the person who owns the harder problems — the ones that need someone who understands the underlying data deeply and can shape it into something the business can use.
Most of your time will be spent working with the raw data: understanding what is in it, what is missing, what is reliable, and what features need to be engineered to make it useful. The rest of your time will be spent partnering with the business — occasionally building dashboards yourself, occasionally guiding more junior analysts who are building them.
Your output is not just dashboards. It is the data layer that everything else is built on. Responsibilities:
Own the underlying data
Develop a deep understanding of the source data.
Identify gaps, inconsistencies, and quality issues; partner with Data Engineering to resolve them at source
Design and build the silver and gold-layer tables that downstream dashboards and analyses rely on
Engineer the features that matter
Translate business concepts into well-defined data features: user segments, lifecycle stages, behavioural scores, profitability metrics
Build the shared dimensions and metric definitions that get reused across the BI portfolio
Ensure features are reproducible, documented, and trusted — so the rest of the team and the business can rely on them
Partner with the business
Work directly with stakeholders across domains to understand the decisions they need to make
Occasionally build dashboards end-to-end — particularly for the more complex, cross-domain, or analytically heavy ones
You are proficient in Python and SQL
Strong, production-grade SQL — including window functions, complex joins, and performance tuning on large datasets
Proficient Python for data work — pandas, PySpark, and the broader analytics ecosystem
Comfortable working in a modern lakehouse environment (Databricks preferred); familiar with Delta tables, medallion architecture, and notebook-driven workflows
You understand data deeply
Track record of working with messy, real-world transactional data and turning it into something the business can use
Experience building reusable data models, semantic layers, or metric definitions
Good instincts for data quality — you spot when a number looks wrong before anyone else does
Competitive salary and benefits package.
Opportunity to work in a fast-paced and innovative environment.
Be part of a growing and dynamic team.
Make a real impact on the company's success.
Various team building programs and company events.
Comprehensive healthcare schemes for employees and dependants.
And many more! Apply and let us tell you more!
#LI-MC1 We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. apply for this job
Qualifications: Full-time, On-site, Senior Data Scientist, Analytics Engineer, Senior individual contributor, deep data understanding, raw data analysis, feature engineering, business partnership, dashboard building, guiding junior analysts, data ownership, source data understanding, identify data gaps/inconsistencies/quality issues, partner with Data Engineering, design/build silver/gold-layer tables, translate business concepts to data features, user segments, lifecycle stages, behavioural scores, profitability metrics, build shared dimensions/metric definitions, ensure reproducible/documented/trusted features, stakeholder collaboration, end-to-end dashboard building, Python proficiency, SQL proficiency, production-grade SQL, window functions, complex joins, performance tuning, large datasets, Python for data (pandas, PySpark, analytics ecosystem), modern lakehouse environment (Databricks preferred), Delta tables, medallion architecture, notebook-driven workflows, experience with messy transactional data, building reusable data models, semantic layers, metric definitions, good data quality instincts