SA, Data Scientist
SA, Data Scientist
Astro2-5 Years
- Posted an hour ago
- Be among the first 10 applicants
Job Description
WHY JOIN US
Job Responsibilities
Audience Segmentation & Customer Analytics
Technical Skills
- 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.
- 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.
- 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.
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:
- Clustering and Segmentation
- Regression Models
- Decision Trees and Random Forests
- Gradient Boosting Models
- Customer Propensity Modeling
- Time Series Analysis
- Recommendation Systems
- 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.
- 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.
- 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.
More Info
Key Skills
Gradient Boosting Models
Clustering and Segmentation
Random Forests
Recommendation Systems
Customer Propensity Modeling
About Company
Skills:
snowflake , Machine Learning, Predictive Modelling, Sql, Google Cloud, Tensorflow, Pytorch, Python, AWS, Airflow, Agentic AI architectures, MLOps orchestration, Statistics, MLflow
Skills:
snowflake , Machine Learning, Predictive Analytics, Sql, Azure ML, MLops, Statistical Modelling, Databricks, Clustering, Python, Generative AI, model deployment, event-triggered workflows, recommendation engines, CI CD, conversational AI solutions, LLM applications, Segmentation, AI agents, Microsoft Fabric, real-time scoring, RAG architectures, model monitoring
Skills:
software engineering practices , Neural Networks, Tensorflow, Deep Learning, Numpy, Pytorch, Python, Machine Learning Algorithms, Apis, Version Control, Devops, Pandas, Decision Trees, Agile Methodologies, Keras, Data Integration, statistical analysis techniques, GenAI, scikit-learn, time-series, ML Ops practices, validation methods, LLMs, Classification, Bayesian techniques, population-based meta-heuristic optimization methods, ensemble methods, Data and Cloud environments, Regression
Skills:
Python, Sql
Skills:
FastAPI, PostgreSQL, Dashboarding, Sql, Docker, Flask, Python, Qdrant, Pinecone, FAISS, Data Storytelling, Milvus, LLM-based structured extraction