Data Scientist

About GoTyme

GoTyme is a joint venture between the Gokongwei Group, one of the biggest conglomerates in the Philippines, and the Singapore-headquartered digital banking group Tyme. This venture combines the trusted Gokongwei brand, customer base, and distribution ecosystem with Tyme’s globally proven digital banking technology and hands-on experience building South Africa’s leading digital bank, TymeBank, one of the fastest-growing digital banks in the world today.

At GoTyme, we have embarked on a journey to democratize financial services and bring next-level banking to the Philippines. We seek individuals who share our belief that the game is worth changing, to join our growing team of GoTymers as we build, launch, and scale a bank that empowers all Filipinos to navigate a path to financial freedom.

About the Role

We are looking for a passionate and energetic Data Science Senior Analyst to turn insights into action and develop data-driven strategies across the bank’s value chain (deposits, savings, lending, and investment products). The ideal candidate will apply themselves to identify and make use of all our fit for purpose internal and external data sources, whether they be structured, unstructured, semi-structured, traditional data sources and /or alternative data sources, to develop best in class solutions and capabilities. As a Data Science Senior Analyst, you will be tasked with solving challenging and complex business problems covering a variety of areas including, but not limited to marketing, credit, and fraud analytics.

Data Analysis and Insights:

• Analyze large datasets to identify patterns, trends, and anomalies across critical bank services (shop, move, save, invest, lend, customer service, fraud prevention, etc.)
• Provide actionable insights and recommendations based on the data analysis which have been performed.
• Setup and execute test and learn programs to optimize bank marketing, acquisitions, customer management, and fraud strategies.
• Identity and onboard new data sources (on-us, off-us: bureau, consortium, device, alternative, psychometric, etc.) that can be leveraged for strategy development.

Performance Monitoring:

• Continuously monitor the performance of bank programs; including, but not limited to marketing/credit/fraud campaigns, strategies, models, policies, and rules.
• Perform regular updates and recalibrations to maintain model/strategy/rule accuracy and effectiveness.
• Develop, maintain, and monitor learning agendas that allow the bank to optimize marketing, credit, and fraud strategies.

Model Development and Maintenance:

• Design, develop and maintain internal predictive models.
• Manage, support and provide oversight on externally-sourced predictive models.
• Introduce new and innovative solutions such as machine learning techniques to enhance strategies where applicable.

Collaboration and Teamwork:

• Collaborate and work closely with customers, partners, and key stakeholders to help the business arrive at positive outcomes.
• Engage and embed yourself in pods to understand the business, gain a feel for what is being seen by the “boots on the ground” to perform analysis that produces the right solutions to the case.

Innovation and Research:

• Stay updated with the latest developments in data science using AI and ML.
• Experiment with new tools, techniques, algorithms and methodologies to enhance the bank’s capabilities.

Requirements

Must Haves

• Bachelor’s degree or higher in applied mathematics, statistics, computer science, physics, data science, economics, engineering or equivalent quantitative discipline 
• 3 to 7 years of related work experience is required.
• Relevant experience or diplomas, certificates or completed training courses in marketing analytics, credit risk management, data science 
• Experience applying data science techniques, developing and implementing advanced and/or machine learning models within an e-commerce or banking environment; with proficiency in most of the following: Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Neural Networks, Clustering, Principal Component Analysis, Factor Analysis, Boosting Algorithms etc. 
• Hands on experience working with, manipulating and analyzing large datasets using programming languages such as SQL, R, SAS, Python, etc. and writing the necessary scripts to extract these successfully and efficiently (without timeouts or memory failures). 
• Demonstrated ability in setting up, running, and evaluating tests (A/B, Champion/Challenger/Test and Control, DoE) to turn insights into action 
• Proficient in supporting analyses and recommendations with the appropriate data visualization using tools like Databricks, PowerBI or Tableau or something similar. 
• Analytical Skills: 
• Strong analytical problem-solving skills. 
• Strong conceptual thinker who provides structured solutions to complex business problems. 
• Analytical animal who translates business problems into equations to drive data driven decision making and interprets complex datasets to find key economic drivers and identify actionable insights. 
• Technical Skills: 
• Proficient in programming languages such as SQL, R, SAS, Python, etc. and experienced with manipulating large datasets. 
• Strong knowledge of statistical concepts, methods and machine learning algorithms. 
• Proven track record of developing, operationalizing, and executing/implementing strategies and models. 
• Communication and Collaboration: 
• Strong communication skills to convey technical concepts to non-technical stakeholders. 
• Possesses the skill to not only pull the data and generate reporting, but also tell the story and extract an insight. 
• Ability to work collaboratively with cross-functional teams an drive positive outcomes for the business. 
• Adaptability and Innovation: 
• Willingness to stay updated with the latest industry trends and technologies. 
• Ability to adapt to changing priorities and work in a fast-paced environment. 
• Attention to Detail: 
• High level of accuracy and attention to detail across data analysis, strategy development, and implementation activities. 
• Rigorous and uncompromising approach to ensuring data quality and integrity.