DATA ANALYST | Predictive Analytics Decision Management

Siegen HR Solutions; Manila; On-site; PHP 1,000,000 - 1,500,000; Full time

Job summary

A data-focused recruitment firm in Manila is seeking an experienced Data Analyst to develop analytic solutions and provide insights for customer acquisition and retention. The role requires a Master’s degree or PhD in a relevant field and 7+ years of experience. Successful candidates will possess strong quantitative skills and expertise in tools like R and SQL. This position is pivotal in driving data-driven strategies and supporting various teams with actionable insights.

Qualifications

• 7+ years' experience in a relevant Data Scientist role post-graduation.
• Expert knowledge of R and proficiency in statistical software.
• Experience working with large data sets and distributed computing tools.

Responsibilities

• Develop analytic solutions for customer acquisition and retention.
• Conduct A/B testing methodologies analysis.
• Prepare data for statistical analysis and machine learning algorithms.

Skills

Quantitative problem solving

Data manipulation

Statistical analysis

Communication skills

Team collaboration

Flexible analytic approach

Structured problem-solving

Passion for data-driven research

Education

Master’s Degree or PhD in a relevant field

Tools

SQL

R

Python

Looker

Job description

DATA ANALYST | Predictive Analytics Decision Management

Job Openings DATA ANALYST | Predictive Analytics Decision Management

About the job DATA ANALYST | Predictive Analytics Decision Management

KEY RESPONSIBILITIES:

• Use appropriate statistical techniques to develop appropriate analytic solutions for informing customer acquisition and customer retention strategies
• Develop appropriate methodology for analyzing and interpreting results from product features A/B testing
• Work on multiple analytic projects such as user segmentations, user engagement to purchase funnel analysis, user life time value analysis
• Provide data, insights and reporting support to various teams
• Drive the collection of new data and the refinement of existing data sources
• Prepare data for statistical analysis and machine learning algorithms
• Maintain and advance analytic database. Conduct periodic data QA
• Working with technical and non-technical customers to design experiments and communicate statistical results
• Leading training and informational sessions on statistics
• Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business

TO QUALIFY:

• Master’s Degree or PhD in a relevant technical field such as Statistics, Math and Computer Science, Mathematics, Economics and Analytics with 7+ years’ experience in a relevant Data Scientist role post-graduation
• Extensive experience solving business problems using quantitative approaches
• Comfort with extracting, manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources
• Strong passion for data driven research for answering hard questions with data
• Early adopters of new tools, technologies, etc.
• Decent understanding of business
• Structured problem-solving skills to translate problems into solutions
• Flexible analytic approach that allows for results at varying levels of precision
• Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner
• Familiarity with relational databases and intermediate+ level knowledge of SQL
• Expert knowledge of R; Experience with other analytic tools such as Looker – a strong plus
• Experience working with large data sets, familiarity/experience working with distributed computing tools a plus (Map/Reduce, Hadoop, Hive, etc.)
• Problem-solving and analytical abilities
• Proficiency in any of the statistical software such as R, S-plus, SAS, STATA, python programming etc.
• Good verbal and written communication skills and an ability to work in a team environment
• A solid grounding in applied statistics including expertise in at least one of the following is a must:
  • Reliability models
  • Markov Models
  • Stochastic models
  • Bayesian modeling
  • Classification models
  • Cluster analysis
  • Neural network
  • Non-parametric methods
  • Time series analysis
  • Forecasting
  • Multivariate statistics
• Experience in building and deploying business relevant analytics assets
• Experience in Applied Machine learning is an added advantage