Sr. Analyst - Data Science & Mining
Bangalore, Karnataka, India
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingYou will collaborate with data engineers, business stakeholders, and fellow data scientists to deliver projects from ideation through to production deployment.
Implement techniques such as hyperparameter tuning, feature engineering, and cross-validation to enhance model accuracy and generalizability.
Work closely with data engineers and DevOps teams to integrate models into business systems, APIs, or real-time applications.
From the employer’s posting
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. 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.
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: Analyse large and complex datasets to uncover trends, patterns, and opportunities. Use statistical methods to interpret results and present actionable recommendations to stakeholders.
What you’ll bring
All qualificationsPreferred experience
- 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.
Qualification wording
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.
Education & alternatives
- 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. - 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.
Tools in this posting
- Python
- R
- AWS
- Azure
- Docker
- Google Cloud (GCP)
- Kubernetes
- MLflow
- Snowflake
- Tableau
- Plotly
- PyTorch
- TensorFlow
- SQL
- NoSQL
- Power BI
- scikit-learn
- Xgboost
Source — Tool mentions in context
- 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.
- 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).
- 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.
- 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.
Job description
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.
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: Analyse 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.
- 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.
Driivz
WHO IS VONTIER
Vontier (NYSE: VNT) is a global technology company powering the way the world moves. We empower businesses in the transport sector to adapt to a fast-changing landscape by uniting productivity, automation and multi-energy technologies.
Our smart, connected solutions serve roadside convenience retail stores, fleet operators, and auto repair technicians. From integrated payments and EV charging software to carwash technology and retail automation, we help customers stay productive and prepared for a rapidly evolving industry.
With decades of expertise and a balanced portfolio, Vontier enables businesses to navigate complexity, unlock growth, and build a cleaner, safer future. Driven by continuous improvement and the dedication of Team Vontier, we empower businesses to think bigger, act boldly, and thrive on the road ahead. Learn more at www.vontier.com
At Vontier, we empower you to steer your career in the direction of success with a dynamic, innovative, and inclusive environment.
Our commitment to personal growth, work-life balance, and collaboration fuels a culture where your contributions drive meaningful change. We provide the roadmap for continuous learning, allowing creativity to flourish and ideas to accelerate into impactful solutions that contribute to a sustainable future.
Join our community of passionate people working together to navigate challenges and seize new opportunities. At Vontier, you are not on this journey alone, we are committed to equipping you with the tools and support you need to fuel your innovation, lead with impact, and thrive both personally and professionally.
Together, let’s power the way the world moves!
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
Complete your application on vontier.taleo.net. The employer’s form will show what is required.
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Source & posting history
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bangalore, Karnataka, India
Working pattern and location restrictions need checking in the full posting.
- Work authorization
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- Status in our records
- Active
- First seen by us
- Oct 6, 2026
- Recorded sightings
- 10
- Last seen by us
- Oct 9, 2026
- Employer says posted
- Sep 30, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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