Data Scientist - India

Description

About Us

Our Data Analytics team thrives at the intersection of curiosity, rigour, and business impact. We are passionate about transforming data into actionable insights, driving strategic decisions and innovation across the organization. As we continue to grow, we seek a highly skilled and motivated Data Scientist to join our dynamic team, eager to shape the future of data-driven solutions.

Position Overview

The Data Scientist will play a pivotal role in building, refining, and deploying advanced models that empower the business with predictive and prescriptive insights. You will collaborate with data engineers, business stakeholders, and fellow data scientists to deliver projects from ideation through to production deployment. You will be deeply involved in every aspect of the data science lifecycle, from data exploration to model operationalisation.

Location - Bangalore

Key Responsibilities

· Model Building: Design and develop robust machine learning and statistical models tailored to solving complex business problems. Select appropriate algorithms, optimise parameters, and rigorously validate models to ensure high performance and reliability.

· Model Refinement: Continuously monitor, test, and improve models based on feedback, new data, or changing business requirements. Implement techniques such as hyperparameter tuning, feature engineering, and cross-validation to enhance model accuracy and generalizability.

· Model Deployment: Deploy models to production environments, ensuring scalability, stability, and maintainability. Work closely with data engineers and DevOps teams to integrate models into business systems, APIs, or real-time applications.

· Data Analysis & Exploration: Analyze large and complex datasets to uncover trends, patterns, and opportunities. Use statistical methods to interpret results and present actionable recommendations to stakeholders.

· Collaboration & Communication: Work cross-functionally with business leaders, product managers, analysts, and engineers to understand requirements, translate business needs into analytical solutions, and clearly communicate findings and recommendations.

· Documentation & Best Practices: Maintain comprehensive documentation for models, codebases, and analytical processes. Promote and adhere to best practices in coding, experimentation, and reproducibility.

· Innovation & Learning: Stay abreast of the latest developments in data science, machine learning, and AI. Proactively identify and evaluate new tools, frameworks, and techniques that can enhance our capabilities.

Requirements

Required Qualifications

· Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related field. PhD is a plus.

· Proven experience (5+ years) in building, refining, and deploying machine learning/statistical models in a professional setting.

· Strong programming skills in Python, R, or similar languages, with proficiency in machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost).

· Solid understanding of data structures, algorithms, and software engineering principles.

· Experience with cloud platforms (e.g., AWS, Azure, GCP) and model deployment tools (e.g., Docker, Kubernetes, MLflow) is highly desirable.

· Familiarity with DWH technology (Snowflake…) and database systems (SQL/NoSQL).

· Strong grasp of statistical concepts, hypothesis testing, and experimental design.

· Excellent problem-solving skills, with the ability to break down complex issues into actionable tasks.

· Outstanding communication skills, with the ability to convey complex technical concepts to non-technical audiences.

· Demonstrated ability to thrive in a collaborative, fast-paced environment.

Preferred Qualifications

· Experience in deploying models into real-time or high-availability production environments.

· Familiarity with MLOps practices and tools.

· Knowledge of data visualisation tools (e.g., Tableau, Power BI, Plotly, Dash).

· Experience with CI/CD pipelines for ML projects.

· Domain expertise in areas such as Energy, Billing & Invoicing, user behavioural analytics.

Core Competencies

· Technical Excellence: Demonstrates expertise in applying data science techniques to solve real-world problems. Continuously seeks to deepen technical skills and stay current with industry trends.

· Business Acumen: Understands business drivers and challenges, aligning analytical approaches with organisational goals.

· Ownership & Accountability: Takes responsibility for deliverables, timelines, and quality of work. Proactively manages and communicates project risks and dependencies.

· Collaboration: Fosters an inclusive and supportive team environment, actively sharing knowledge and seeking input from colleagues.

· Adaptability: Embraces change, learns quickly, and is willing to iterate in response to feedback or shifting priorities.