Data Scientist

As a Data Engineer at Ascentech Services Limited, you will play a crucial role in designing, building, and maintaining large-scale data systems. Your primary responsibility will be to connect and model complex distributed data sets to build repositories, such as data warehouses and data lakes, using appropriate technologies.

• Define data lifecycle, including data models and data sources for analytics platforms, gathering data from business and cleaning the data.
• Create and maintain optimal data pipeline architecture.
• Conduct advanced statistical analysis to provide actionable insights, identify trends, and measure performance.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS 'big data' technologies.
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
• Perform aggregations on data across various warehousing models.
• Work with stakeholders, including Executives, Product, Data, and Design teams, to assist with data-related technical issues and support their data infrastructure needs.
• Keep data secure.
• Create data tools for analytics and data scientist team members.
• Work closely with database teams on topics related to data requirements, cleanliness, accuracy, etc.

• 5-7 years' experience in a statistical and/or data science role.
• Experience working with large data sets, simulation/optimization, and distributed computing tools.
• Practical knowledge across data extraction and transformation tools - traditional ETL tools (Informatica, Altryx) as well as more recent big data tools.
• Experience in working with large datasets, relational databases (SQL), and distributed systems (Hadoop, Hive).
• Experience Proficiency in R and/or SAS; additional knowledge of other statistical software (SPSS, RapidMiner, etc.) helpful.
• Knowledge in data architecture, defining data retention policies, monitoring performance, and advising any necessary infrastructure changes.
• Comfortable in dashboard development (Tableau, Powerbi, Qlik, etc.) and in developing data analytics models (R, Python, Spark).
• Solid business and collaboration skills, and responsive to service needs and operational demand.