Junior Data Science Developer

What role will you play in the team

As a Junior Data Science Developer, you will become an integral part of our core analytics and machine learning task force. Your primary focus will be engineering robust data pipelines, implementing advanced statistical models, and translating complex business requirements into tangible, data-driven software components that empower organizational decision-making across multiple verticals.

What you will do

You will design and execute comprehensive ETL workflows, aggregate massive structured and unstructured datasets, and build exploratory data models. Your routine will involve querying large relational and non-relational databases using SQL and MongoDB, building interactive dashboards with Power BI or Tableau, and validating statistical hypotheses using Python and specialized data science libraries.

Key responsibility

• Develop and maintain scalable ETL pipelines utilizing tools like Talend, Informatica, and Apache Nifi to streamline data ingestion from multiple disparate sources.
• Perform advanced statistical analysis and hypothesis testing using Python, R, and specialized statistical software to uncover deep hidden business trends and patterns.
• Build, test, and deploy supervised and unsupervised machine learning algorithms using scikit-learn and PyTorch to automate core predictive analytics functions.
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  Design interactive executive dashboards and dynamic data visualization reports utilizing Power BI and Tableau to communicate complex metrics effectively to stakeholders.
• Manage, query, and optimize large-scale databases using advanced SQL, PostgreSQL, MySQL, and MongoDB to ensure high-speed data retrieval operations.
• Implement big data processing frameworks including Hadoop, Spark, and Apache Kafka to handle high-volume streaming datasets with optimal computational efficiency.

Required Qualification and Skills

• Master's or Bachelor's degree in Data Science, Information Technology, Computer Engineering, Statistics, or a related quantitative analytical field of study.
• Professional or substantial project experience ranging between 1 to 2 years in data engineering, statistical modeling, or core machine learning development.
• Advanced proficiency in programming languages specifically Python and R, alongside strong capabilities in writing optimized database queries.
• Extensive practical experience with data visualization tools including Tableau, Power BI, and Google Data Studio for enterprise reporting.
• Solid working knowledge of database management systems such as SQL, MongoDB, PostgreSQL, MySQL, and foundational big data technologies like Apache Spark.

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  Familiarity with ETL tools like Talend and Informatica, and machine learning frameworks such as scikit-learn, TensorFlow, and Keras.

Benefits Included

• Competitive base compensation paired with attractive performance incentives and annual retention bonuses based on project delivery success.
• Robust health, life, and accident insurance packages covering both outpatient and inpatient medical expenses for employees and dependents.
• Flexible work culture complemented by hybrid work policy options, wellness stipends, and dedicated mental health support programs.
• Ample opportunities for rapid career advancement, leadership training programs, and fully funded technical conference participations.
• Dynamic, high-energy work environment featuring modern collaborative zones, stocked cafeterias, and team-building recreational events.

A Day in the Life

You kick off your morning by analyzing overnight batch data processing logs from automated ETL pipelines built on Apache Spark. Following this, you spend a few hours writing complex SQL queries to extract customer behavior data for a new segmentation model. In the afternoon, you collaborate with the visualization team to update executive Power BI dashboards, finishing your day by optimizing a Python script used for processing streaming data via Apache Kafka.