Data Science Manager
Globe Telecom
Data Science Manager
Job Description
At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal. Job Description The Data Science Manager leads the end-to-end development of data-driven solutions, from translating business needs into data science projects, to building, deploying, and monitoring predictive models in production. This role ensures models deliver measurable business impact, maintains the reliability of machine learning pipelines, and collaborates closely with business, product, and engineering teams to integrate data science solutions into operational systems.DUTIES AND RESPONSIBILITIES:Data Exploration & Feature EngineeringLead data extraction, exploration, cleansing, and transformation of large and complex datasetsDesign, engineer, and validate features needed for predictive models and advanced analytics Build frameworks and data pipelines that combine telco datasets with digital, social, and external data sources to create a holistic customer view.Model Development & Performance ManagementTranslate business problems into clear data science approaches and model requirements Build, test, and deploy machine learning and statistical models that address business needsTrack performance, accuracy, drift, and business value of deployed modelsConduct periodic model tuning and ensure continuous improvement aligned with ROI goals.Insights, Applications & Business EnablementTranslate model outputs into clear insights, actionable recommendations, and campaign or operational strategies Identify new opportunities to apply data science, especially in customer behavior prediction, segmentation, and credit risk scoring.Partner with business teams to embed analytics solutions into decision-making and customer lifecycle programs.REQUIREMENTS:Work ExperienceMinimum of three (3) years’ experience in customer analytics domain and/or credit risk assessment and financial services, covering most of the following: data mining, predictive modeling, machine learning, statistical modeling and analysis, large scale data acquisition, transformation, and cleaning, both structured and unstructured dataProven track record of leading and collaborating on advanced analytics strategic initiatives; Proven track record of operationalization of analytic models in collaboration with marketing/risk and IT teamsWorked with large, unfiltered data sets or data science researchLevel of KnowledgeHas Knowledge of both structured and unstructured dataMust possess core competencies, deep understanding and relevant experience inScripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, Java, Ruby, Clojure, Matlab, Pig, SQL);Statistical Analysis: advanced usage of off-the-shelf tools such as R, SAS, SPSS, Weka and other analytical tools or softwareBig Data: Experience with Big data tools such as HDFS, Cassandra, StormDatabase knowledge: skilled in structured databaseFamiliar with most of the following disciplines:Conceptual modeling: to be able to share and articulate modeling;Predictive modeling: most of the big data problems are towards being able to predict future outcomes;Hypothesis testing: being able to develop hypothesis and test them with careful experiments;Natural Language Processing: the interactions between computer and humans;Machine learning: using computers to improve as well as develop algorithms;Statistical analysis: to understand and work around possible limitations in models.EducationDegree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering, Computer Science, Econometrics or Information Science such as Business Analytics or InformaticsEqual Opportunity EmployerGlobe’s hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.Globe’s Diversity, Equity and Inclusion Policy Commitment can be accessed hereMake Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.
Locations
- 21F The Globe Tower
Skills Required
- customer analytics domain and/or credit risk assessmentintermediate
- both structuredintermediate
- Big data tools such as HDFSintermediate
- structured databaseintermediate
Required Qualifications
- Minimum of three (3) years’ experience in customer analytics domain and/or credit risk assessment and financial services, covering most of the following: data mining, predictive modeling, machine learning, statistical modeling and analysis, large scale data acquisition, transformation, and cleaning, both structured and unstructured data (experience)
- Proven track record of leading and collaborating on advanced analytics strategic initiatives; Proven track record of operationalization of analytic models in collaboration with marketing/risk and IT teams (experience)
- Worked with large, unfiltered data sets or data science research (experience)
- Minimum of three (3) years’ experience in customer analytics domain and/or credit risk assessment and financial services, covering most of the following: data mining, predictive modeling, machine learning, statistical modeling and analysis, large scale data acquisition, transformation, and cleaning, both structured and unstructured data (experience)
- Proven track record of leading and collaborating on advanced analytics strategic initiatives; Proven track record of operationalization of analytic models in collaboration with marketing/risk and IT teams (experience)
- Worked with large, unfiltered data sets or data science research (experience)
- Level of Knowledge (experience)
- Has Knowledge of both structured and unstructured data (experience)
- Must possess core competencies, deep understanding and relevant experience in (experience)
- Scripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, Java, Ruby, Clojure, Matlab, Pig, SQL); (experience)
- Statistical Analysis: advanced usage of off-the-shelf tools such as R, SAS, SPSS, Weka and other analytical tools or software (experience)
- Big Data: Experience with Big data tools such as HDFS, Cassandra, Storm (experience)
- Database knowledge: skilled in structured database (experience)
- Familiar with most of the following disciplines: (experience)
- Conceptual modeling: to be able to share and articulate modeling; (experience)
- Predictive modeling: most of the big data problems are towards being able to predict future outcomes; (experience)
- Hypothesis testing: being able to develop hypothesis and test them with careful experiments; (experience)
- Natural Language Processing: the interactions between computer and humans; (experience)
- Machine learning: using computers to improve as well as develop algorithms; (experience)
- Statistical analysis: to understand and work around possible limitations in models. (experience)
- Has Knowledge of both structured and unstructured data (experience)
- Must possess core competencies, deep understanding and relevant experience in (experience)
- Scripting or programming experience: familiarity in programming languages with relational databases (e.g. Python, Java, Ruby, Clojure, Matlab, Pig, SQL); (experience)
- Statistical Analysis: advanced usage of off-the-shelf tools such as R, SAS, SPSS, Weka and other analytical tools or software (experience)
- Big Data: Experience with Big data tools such as HDFS, Cassandra, Storm (experience)
- Database knowledge: skilled in structured database (experience)
- Familiar with most of the following disciplines: (experience)
- Conceptual modeling: to be able to share and articulate modeling; (experience)
- Predictive modeling: most of the big data problems are towards being able to predict future outcomes; (experience)
- Hypothesis testing: being able to develop hypothesis and test them with careful experiments; (experience)
- Natural Language Processing: the interactions between computer and humans; (experience)
- Machine learning: using computers to improve as well as develop algorithms; (experience)
- Statistical analysis: to understand and work around possible limitations in models. (experience)
- Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering, Computer Science, Econometrics or Information Science such as Business Analytics or Informatics (degree in quantitative discipline such as statistics)
- Degree in quantitative discipline such as Statistics, mathematics, Operations Research, Engineering, Computer Science, Econometrics or Information Science such as Business Analytics or Informatics (degree in quantitative discipline such as statistics)
- Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us. (experience)
Responsibilities
- Data Exploration & Feature Engineering
- Lead data extraction, exploration, cleansing, and transformation of large and complex datasets
- Design, engineer, and validate features needed for predictive models and advanced analytics
- Build frameworks and data pipelines that combine telco datasets with digital, social, and external data sources to create a holistic customer view.
- Lead data extraction, exploration, cleansing, and transformation of large and complex datasets
- Design, engineer, and validate features needed for predictive models and advanced analytics
- Build frameworks and data pipelines that combine telco datasets with digital, social, and external data sources to create a holistic customer view.
- Model Development & Performance Management
- Translate business problems into clear data science approaches and model requirements
- Build, test, and deploy machine learning and statistical models that address business needs
- Track performance, accuracy, drift, and business value of deployed models
- Conduct periodic model tuning and ensure continuous improvement aligned with ROI goals.
- Translate business problems into clear data science approaches and model requirements
- Build, test, and deploy machine learning and statistical models that address business needs
- Track performance, accuracy, drift, and business value of deployed models
- Conduct periodic model tuning and ensure continuous improvement aligned with ROI goals.
- Insights, Applications & Business Enablement
- Translate model outputs into clear insights, actionable recommendations, and campaign or operational strategies
- Identify new opportunities to apply data science, especially in customer behavior prediction, segmentation, and credit risk scoring.
- Partner with business teams to embed analytics solutions into decision-making and customer lifecycle programs.
- Translate model outputs into clear insights, actionable recommendations, and campaign or operational strategies
- Identify new opportunities to apply data science, especially in customer behavior prediction, segmentation, and credit risk scoring.
- Partner with business teams to embed analytics solutions into decision-making and customer lifecycle programs.
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