SA, Data Scientist

WHY JOIN US

• We practice a vibrant & energetic office culture.

• We provide opportunities for career advancement within the company.

• Good performance is always rewarded accordingly.

JOB RESPONSIBILITIES:

Audience Segmentation & Customer Analytics

. Develop and maintain PayTV and Digital audience segments using behavioral, demographic, transactional, and engagement data.

. Build customer profiles and audience taxonomies to support personalization, targeting, and campaign optimization.

. Perform audience sizing, profiling, and overlap analysis across multiple platforms and products.

. Support the development of a unified audience view by integrating customer, viewing, digital, and third-party data sources.

Data Science & Modeling

. Apply statistical and machine learning techniques to identify audience clusters, customer affinities, and engagement drivers.

. Develop propensity, churn, content affinity, and customer value models to support business use cases.

. Conduct exploratory data analysis to uncover trends, patterns, and growth opportunities.

. Evaluate model performance and continuously improve segmentation methodologies.

Business Insights & Stakeholder Support

. Translate complex analytical findings into actionable business recommendations.

. Collaborate closely with Marketing, Product, Media Sales, and Content teams to address audience-related business challenges.

. Support campaign measurement, audience effectiveness analysis, and customer engagement initiatives.

. Prepare executive-ready reports, and presentations that communicate key insights and opportunities.

REQUIREMENTS:

Technical Skills

. Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field.

. 2-5 years of experience in data analytics, customer analytics, data science, or audience measurement.

. Proficiency in Python and SQL for data extraction, processing, and analysis.

. Experience working with large-scale datasets in cloud-based data environments (AWS, Databricks, Azure, Snowflake, Redshift, Spark, etc.).

. Familiarity with machine learning and statistical techniques such as:

o Clustering and Segmentation

o Regression Models

o Decision Trees and Random Forests

o Gradient Boosting Models

o Customer Propensity Modeling

o Time Series Analysis

o Recommendation Systems

Audience & Business Analytics

. Experience in customer segmentation, audience measurement, customer insights, CRM analytics, or digital analytics.

. Understanding of customer lifecycle management, audience activation, personalization, and campaign analytics.

. Experience working with digital, streaming, media, content, telecommunications, or subscription-based businesses is an advantage.

. Familiarity with audience measurement tools, web analytics, or CDP/DMP platforms is preferred.

Visualization & Communication

. Experience using BI and visualization tools such as Power BI, Tableau, QuickSight, or similar platforms.

. Strong storytelling skills with the ability to communicate analytical insights to both technical and non-technical stakeholders.

. Excellent presentation, written, and verbal communication skills.

Core Competencies

. Strong analytical and problem-solving mindset.

. Ability to translate business requirements into data-driven solutions.

. Self-motivated, curious, and eager to learn new technologies and analytical approaches.

. Ability to manage multiple priorities in a fast-paced, cross-functional environment.

. Strong stakeholder management and collaboration skills.rial engineering)

• 2 years of relevant work experience in data analysis or related field. (e.g., as a statistician / data scientist / computational biologist / bioinformatician).
• Strong Machine Learning and Data Mining background. Including ability to build and interpret machine learning models of complex, high-dimensional systems.
• Experience processing large-scale data prototyping, then production-level algorithms.
• Ability to work with incomplete or imperfect data to extract usable information.
• Attention to detail, data accuracy and quality of output.
• Experience with large scale distributed data processing frameworks like Hadoop and Spark
• Experience of high-level programming language for analysis (e.g. Spark, Scala, Python, R, Java) a plus
• Highly self-motivated, results driven and data driven. Ability to work in a fast-paced dynamic environment.
• Experience in audience and payment sciences is a plus.
• Traits
• Be positive, passionate, collaborative, self-motivated, responsible, dependable and enthusiastic team player.
• Experience articulating business questions and using mathematical techniques to arrive at an answer using available data. Experience translating analysis results into business recommendations.
• Demonstrated skills in selecting the right statistical tools given a data analysis problem. Demonstrated effective written and verbal communication skills.
• Demonstrated leadership and self-direction. Demonstrated willingness to both teach others and learn new techniques.