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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.

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.
 


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 (3+ 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 big data technologies (e.g., Spark, Hadoop) 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 finance, healthcare, retail, or marketing analytics.
•    Experience in the Manufacturing domain is highly preferred.
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.


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WHO IS VONTIER

Vontier (NYSE: VNT) is a global industrial technology company uniting productivity, automation and multi-energy technologies to meet the needs of a rapidly evolving, more connected mobility ecosystem. Leveraging leading market positions, decades of domain expertise and unparalleled portfolio breadth, Vontier enables the way the world moves – delivering smart, safe and sustainable solutions to our customers and the planet. Vontier has a culture of continuous improvement and innovation built upon the foundation of the Vontier Business System and embraced by colleagues worldwide. Additional information about Vontier is available on the Company’s website 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 who work together to navigate challenges and seize opportunities.  At Vontier, you are not on this journey alone-we are dedicated to equipping you with the tools and support needed to fuel your innovation, lead with impact, and thrive both personally and professionally.

Together, let’s enable the way the world moves!

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