About the Role
We are seeking an experienced and results-driven Lead Data Scientist to join our growing analytics team. In this role, you will work at the intersection of data, business, and technology to drive smarter decision-making and strategic initiatives. You’ll develop Agentic AI agents, predictive models, perform complex analyses, and deliver insights that power key decisions related to customer acquisition, product performance, and regional financial optimization. This is a hands-on, cross-functional role that requires expertise in machine learning, data engineering, and stakeholder engagement. If you are passionate about solving real-world business problems through data, this role is for you.
Key Responsibilities
- Design and Develops Agentic AI framework and Agents.
- Analyze large, complex datasets to uncover actionable insights that drive business impact.
- Design and implement predictive models, especially in areas such as customer penetration optimization and regional performance forecasting.
- Apply machine learning techniques—including supervised and unsupervised learning—to solve critical business problems (e.g., classification, regression, clustering).
- Develop challenger models to test and improve model accuracy and relevance.
- Engineer meaningful features from raw data to optimize model performance.
- Conduct exploratory data analysis (EDA) to identify trends, anomalies, and business opportunities.
- Collaborate across product, engineering, and business teams to translate insights into clear, actionable strategies.
- Use visualization tools like Tableau, Power BI, and Google Analytics to communicate insights and model outcomes.
- Stay current with ML/AI innovations and recommend tool or process improvements.
- Document methodologies and maintain version control for all modeling projects.
- Drive experimentation (A/B testing, causal inference frameworks) to measure business impact and validate hypotheses.
- Champion adoption of new tools, frameworks, and methodologies to keep the team at the forefront of analytics innovation.
What You Bring
Experience
- 7+ years of overall IT or data experience.
- 2 to 4 years specifically in data science, predictive modeling, or ML engineering.
- Telecom Industry Experience (2–4 Years) Is Highly Preferred.
Technical Skills
- Programming: Python (NumPy, Pandas, Scikit-learn, TensorFlow), R (ggplot2, caret)
- ML Platforms: Azure ML Studio, Jupyter Notebook, RStudio, Anaconda
- Visualization: Tableau, Power BI, Google Analytics
- Databases: SQL (Snowflake, MySQL), NoSQL (MongoDB)
- Deployment: Experience with GitLab, Java APIs, and Spring Boot integration
- Statistical Techniques: A/B testing, hypothesis testing, time-series modeling (ARIMA, LSTM), anomaly detection (Isolation Forest, Autoencoders, One-Class SVM)
Soft Skills
- Strong problem-solving and critical thinking skills
- Ability to break down complex technical topics for business stakeholders
- Comfortable working in a fast-paced, agile environment with shifting priorities
Qualifications
- Bachelor’s degree in Computer Science or related field.
- Master’s degree or equivalent advanced degree preferred.
- Proven track record of delivering data science projects from ideation to production.
- Strong communication skills and the ability to tell compelling stories with data.
- Comfortable with both structured and unstructured data sets.
Job Overview
We are seeking an experienced and results-driven Data Scientist to join our growing analytics team. As a Data Scientist, you will work at the intersection of data, business, and technology. Your role is to extract actionable insights from large volumes of structured and unstructured data, develop predictive and different types of models, and contribute to strategic decision-making through advanced analytics and machine learning.
You will partner with cross-functional teams including engineering, product management, and business operations to create scalable, production-grade models that support the company’s Digital First Approach, enabling innovation and enhancing customer experiences.
Key Responsibilities
- Conduct end-to-end data science projects: from understanding business needs to delivering ML solutions that solve real-world problems.
- Perform exploratory data analysis (EDA) to identify trends, patterns, and outliers in large datasets.
- Engineer meaningful features from raw data to improve model performance.
- Develop and validate predictive models using supervised and unsupervised learning algorithms.
- Apply advanced statistical techniques and machine learning methods to solve classification, regression, and clustering problems.
- Utilize and compare various algorithms including Isolation Forest, Autoencoders, One-Class SVM, ARIMA, and LSTM-based models.
- Collaborate with data engineers and software developers to deploy models in production using tools like Spring Boot, GitLab, and APIs.
- Evaluate and monitor model performance over time and recommend improvements.
- Use visualization platforms like Tableau, Power BI, and Google Analytics to communicate insights and findings to business stakeholders and leadership.
- Maintain proper documentation for all models and methodologies used.
- Stay current with advancements in ML/AI tools, libraries, and techniques.
- Participate in Agile ceremonies and manage tasks using Jira.
Technical Skills & Tools
- Languages: Python (NumPy, Pandas, Scikit-learn, TensorFlow, etc.), R (ggplot2, caret, etc.)
- ML Platforms & IDEs: Azure ML Studio, Jupyter Notebook, RStudio, Anaconda
- Modeling: Supervised and unsupervised methods
- Visualization: Tableau, Power BI, Google Analytics
- Database Knowledge: SQL (Snowflake, MySQL), NoSQL (MongoDB)
- Deployment & Integration: Understanding of Java APIs, Spring Boot, and GitLab for model integration
- Statistical Analysis: Hypothesis testing, A/B testing, regression, classification, clustering
Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- Proven experience as a Data Scientist or similar role.
- Proficiency in Python, R, SQL, and data manipulation libraries (e.g., pandas, numpy).
- Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Familiarity with data visualization tools (e.g., Tableau, Power BI, matplotlib, seaborn).
- Strong understanding of statistics, probability, and mathematical modeling.
- Experience working with structured and unstructured data
- Ability to convey complex technical results in a clear and concise manner
Nice to Have
- Experience in telecom domain
- Exposure to big data tools such as Apache Spark or Hadoop
- Knowledge of CI/CD practices in data science