Data Scientist

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The Data Scientist is responsible for the effective execution of a wide range of analytics projects within the PH Finance Analytics hub as guided by the CRISP-DM Process.

Drawing on his/her deep technical expertise in data analysis and synthesis, statistical programming and modeling, among other quantitative methods, the jobholder performs a wide range of analytics techniques in order to deliver successful solutions to identified business problems. He/she also actively contributes in shaping laymanized analytics results so that these can be communicated to business stakeholders.

PRIMARY RESPONSIBILITIES

Data Modeling

1. Business Understanding. Works closely with different stakeholders to understand problems and hypotheses that are translatable into use cases for advanced analytics. Collaborates with them in framing business problems and analytic objectives.
2. Data Understanding. Diagnoses the strengths and limitations of datasets as raw material to a particular analytics project. Recommends various options for data sources and contributes to the identification of the most appropriate datasets.
3. Data Modeling. Performs data preparation and wrangling activities and develops data models by leveraging techniques in data analysis, data mining, and statistical or machine learning algorithms, among other quantitative methods.
4. Model Deployment and Maintenance. Extracts actionable insights from data models in order to incite strategic or tactical action. Works with BT on re-encoding prototype models and production-level models, in some cases, so that they can be used operationally by the business.

Data Planning

1. Optimization of Data Sources. Provides valuable input in the identification of new or enhanced mechanisms to collect both internal and external data, that would benefit analytics projects and their respective modeling requirements.
2. Design of Experiments. Contributes in the conceptualization of experiments that are designed to collect relevant data (e.g., A/B testing), where appropriate, and assists in the development of the business case for such undertakings.
3. External Scanning. Collects and compiles macroeconomic, industry, competitor, and other external data, and analyzes business and financial performance in the appropriate external context.

Value Generation

1. Value Generation. Executes analytics projects that contribute to revenue and profit goals, either by maximizing revenues or by optimizing costs and underlying cost drivers.
2. Cost-vs-Benefit and Resource Allocation. Participates in the development of business cases that present the relevant costs of data and of analytics projects vs. the business value that may be expected from them.

Capability Building and Continuing Education

1. Thought Leadership. Demonstrates the value of advanced finance analytics tools and methodologies through communicating analytics results and their wide-reaching impact and benefits.
2. Analytics Capability Building. Facilitates business and technical knowledge transfer. Fosters an environment where best practices can be shared and improved upon.
3. Continuing Education. Keeps an eye out on the latest analytics trends and the most recent developments in the field. Actively pursues learning opportunities to upskill oneself in the three main skill areas surrounding data analytics: (1) domain expertise; (2) mathematical proficiency; (3) technology and computing.

JOB QUALIFICATIONS:

Education: Bachelor's degree, which should be in a quantitative field of study. An advanced degree is a plus.

Experience: At least two (2) years of cumulative relevant experience in large Philippine or multinational companies.

License/Certification/Training: Analytics certifications are an advantage, but relevant work experience in analytics brings in more weight.

TECHNICAL COMPETENCIES

Quantitative Orientation and Project Management

1. Proficiency in mathematics and statistics.
2. Strong analytical and problem solving skills.
3. Critical thinking, resourcefulness, and solid project management skills.
4. Adequate exposure to the business and industry contexts.

Technical Expertise

1. Strong command of a wide range of advanced analytics techniques, as well as their underlying interpretations and implications.
2. Experience working with programming languages for data analysis (preferably in R or Python, including relevant IDEs and software packages).
3. Data visualization skills (preferably in Power BI, or any similar platform).
4. Database management and querying skills (SQL).
5. Proficiency in MS Office 365 applications.