Data Scientist

Driivz

. Design and develop robust machine learning and statistical models for complex business problems .

Posted 10/7/2026full-timeBangalore • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, developing, and deploying machine learning and statistical models, with a strong foundation in programming and data analysis. Proficient in collaborating with cross-functional teams to translate business needs into actionable analytical solutions.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingStatistical AnalysisModel Deployment (Docker, Kubernetes)Cloud Platforms (AWS, Azure, GCP)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingHyperparameter TuningFeature EngineeringData AnalysisSQLNoSQLData StructuresAlgorithmsExperimental Design

Soft Skills

CollaborationCommunicationProblem-SolvingAdaptabilityAnalytical Thinking

Tools & Technologies

Scikit-learnTensorFlowPyTorchXGBoostDockerKubernetesMLflowTableauPower BISnowflake

Industry Keywords

MLOpsData VisualizationCI/CD PipelinesEnergyBilling & Invoicing

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesNoSQLPythonPyTorchScikit-LearnSQLTableauTensorflow

About the role

Key responsibilities & impact

Design and develop robust machine learning and statistical models for complex business problems
Select algorithms, optimise parameters, and validate models for performance and reliability
Monitor, test, and improve models using hyperparameter tuning, feature engineering, and cross-validation
Deploy models to production environments for scalability, stability, and maintainability
Integrate models into business systems, APIs, or real-time applications with data engineering and DevOps teams
Analyze large and complex datasets to uncover trends, patterns, and opportunities
Apply statistical methods and present actionable recommendations to stakeholders
Collaborate with business leaders, product managers, analysts, engineers, data engineers, and data scientists
Translate business needs into analytical solutions and communicate findings and recommendations
Maintain documentation for models, codebases, and analytical processes
Promote best practices in coding, experimentation, and reproducibility
Stay current with data science, machine learning, and AI developments
Identify and evaluate new tools, frameworks, and techniques

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related field; PhD is a plus
5+ years of experience building, refining, and deploying machine learning/statistical models in a professional setting
Strong programming skills in Python, R, or similar languages
Proficiency with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, and XGBoost
Understanding of data structures, algorithms, and software engineering principles
Experience with cloud platforms such as AWS, Azure, or GCP is highly desirable
Experience with model deployment tools such as Docker, Kubernetes, or MLflow is highly desirable
Familiarity with DWH technology such as Snowflake and database systems including SQL/NoSQL
Strong grasp of statistical concepts, hypothesis testing, and experimental design
Ability to break down complex issues into actionable tasks
Ability to convey complex technical concepts to non-technical audiences
Ability to thrive in a collaborative, fast-paced environment
Preferred: experience deploying models into real-time or high-availability production environments
Preferred: familiarity with MLOps practices and tools
Preferred: knowledge of data visualisation tools such as Tableau, Power BI, Plotly, or Dash
Preferred: experience with CI/CD pipelines for ML projects
Preferred: domain expertise in Energy, Billing & Invoicing, or user behavioural analytics

Benefits

Comp & perks

Remote work arrangement
Hybrid work arrangement