Job Title: Data Scientist (3 to 8 Yrs)
Location: Bangalore, Hybrid
Preferred Skills - Telecom experience (e.g., working with network data, customer analytics) is a plus.
Please note This role requires F2F(In-Person) Interview in Bangalore office.
Why should you choose us
Rakuten Symphony is a Rakuten Group company, that provides global B2B services for the mobile telco industry and enables next-generation, cloud-based, international mobile services. Building on the technology Rakuten used to launch Japan's newest mobile network, we are taking our mobile offering global. To support our ambitions to provide an innovative cloud-native telco platform for our customers, Rakuten Symphony is looking to recruit and develop top talent from around the globe. We are looking for individuals to join our team across all functional areas of our business from sales to engineering, support functions to product development. Let's build the future of mobile telecommunications together!
What Do We Expect From You
We are seeking a skilled and motivated Data Scientist to join our team in India. The ideal candidate will play a key role in designing, building, and deploying advanced data-driven solutions to support business decision-making and enhance mobile network performance. You'll collaborate with cross-functional teams to transform data into actionable insights that drive innovation across Rakuten Mobile's operations..
Roles & Responsibilities:
Data Modeling & Analysis
- Design and develop statistical and machine learning models to solve complex business problems in telecom, including customer behavior analysis, network optimization, and fraud detection.
- Perform exploratory data analysis, data wrangling, and feature engineering to prepare high-quality datasets for modeling.
Model Development & Deployment
- Build, train, evaluate, and deploy predictive models using Python and industry-standard ML frameworks.
- Work with engineering teams to deploy models into production environments, ensuring scalability and performance.
Business Collaboration
- Partner with business stakeholders, product managers, and engineers to understand business needs and translate them into data science solutions.
- Present insights and model results in a clear and concise manner to both technical and non-technical audiences.
Data Infrastructure & Tooling
- Utilize cloud platforms (AWS, GCP, Azure) to build scalable data pipelines and ML workflows.
- Leverage tools like TensorFlow, PyTorch, Scikit-learn, and Spark to handle large-scale data processing and modeling.
Continuous Improvement & Research
- Stay current with the latest developments in AI/ML and apply new techniques to telecom-specific challenges.
- Contribute to the development of best practices and guidelines for data science within the team.
Skills and Qualifications:
Education
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
Technical Expertise
- Strong foundation in statistics, machine learning, and data mining techniques.
- Proficiency in Python and relevant libraries (Pandas, NumPy, Scikit-learn, etc.).
- Experience with ML frameworks (TensorFlow, PyTorch) and data processing tools (Spark, SQL).
- Hands-on experience with cloud services and data pipelines (AWS, GCP, or Azure).
- Telecom experience (e.g., working with network data, customer analytics) is a plus.
Analytical & Problem-Solving Skills
- Ability to analyze large datasets, extract insights, and apply them to solve business problems.
- Strong critical thinking skills with attention to detail and accuracy.
Communication & Collaboration
- Effective communicator with the ability to explain complex concepts clearly.
- Comfortable working in cross-functional teams and contributing to a collaborative environment.
Preferred Qualifications
- Experience in telecom use cases such as customer churn prediction, network performance analysis, and anomaly detection.
- Familiarity with containerization and deployment tools (Docker, Kubernetes).
- Knowledge of CI/CD practices and MLOps frameworks for model lifecycle management.
- Exposure to NLP, time-series forecasting, or deep learning techniques.
Rakuten Shugi Principles:
- Our worldwide practices describe specific behaviours that make Rakuten unique and united across the world. We expect Rakuten employees to model these 5 Shugi Principles of Success.
- Always improve, always advance. Only be satisfied with complete success - Kaizen.
- Be passionately professional. Take an uncompromising approach to your work and be determined to be the best.
- Hypothesize - Practice - Validate - Shikumika. Use the Rakuten Cycle to success in unknown territory.
- Maximize Customer Satisfaction. The greatest satisfaction for workers in a service industry is to see their customers smile.
- Speed!! Speed!! Speed!! Always be conscious of time. Take charge, set clear goals, and engage your team.
