Syngenta Group

Data Scientist

Reads like a ML Engineer role: putting models into production.

Pune, Asia, India

Live, first seen by us 33 days ago

Pay: Pay not listed
Work setting: Not stated
Employment: Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting

What you’ll work on

Full posting

Lead DS and ML predictive analytics solutions, owning technical strategy and delivery for data science projects of varying complexity.

Serve as technical lead for data science squads — driving architecture decisions, code quality standards, and delivery excellence across the team.

Build and deploy predictive models and lead technical workstreams
Set up CI/CD, model versioning and automated retraining pipelines
Establish model monitoring and drift detection in production
Translate business problems into data science problem statements
Mentor junior and mid-level data scientists through code reviews

From the employer’s posting

Lead DS and ML predictive analytics solutions, owning technical strategy and delivery for data science projects of varying complexity.

Serve as technical lead for data science squads — driving architecture decisions, code quality standards, and delivery excellence across the team.

Build and deploy predictive models and analytics capabilities, leading technical workstreams independently

Implement CI/CD practices for model deployment, versioning, and automated retraining pipelines.

Establish model monitoring, drift detection, and performance tracking frameworks in production.

translating business problems into clear data science problem statements

Mentor and develop junior and mid-level data scientists through code reviews, knowledge sessions, and project coaching.

What you’ll bring

All qualifications

Core experience

Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, or related quantitative field
Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks)
3-5 years professional experience delivering production data science solutions
Experience with ML frameworks, version control (Git), and CI/CD for model deployment
Demonstrated leadership through mentoring, technical presentations, or cross-functional project ownership

Preferred experience

Master's preferred

Qualification wording

Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, or related quantitative field (Master's preferred)

Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks)

3-5 years professional experience delivering production data science solutions

Experience with ML frameworks, version control (Git), and CI/CD for model deployment

Demonstrated leadership through mentoring, technical presentations, or cross-functional project ownership

Education & alternatives

Qualifications - Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, or related quantitative field (Master's preferred) - 3-5 years professional experience delivering production data science solutions

Apply at Syngenta Group

Posting record

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Status in our records: Live
First seen by us: Oct 1, 202633 days ago
Recorded sightings: 276
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 33

Similar jobs usually come down between day 6 and day 69.

From 4,296 similar postings that came down. Coming down is not always a hire.

Syngenta Group is hiring

19 open jobs

Open jobs by the week we first saw them, last 12 weeks.

ML Engineer hiring is picking up
Takes jobs down quickly
16% of open jobs were reposted

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
AWS
Azure
Databricks
NumPy
pandas
PyTorch
scikit-learn
TensorFlow

Where the posting names them

- Proven track record leading analytical projects with measurable business impact - Advanced proficiency with Python ecosystem (scikit-learn, TensorFlow/PyTorch, pandas, numpy) - Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks)

- Advanced proficiency with Python ecosystem (scikit-learn, TensorFlow/PyTorch, pandas, numpy) - Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks) - Experience with ML frameworks, version control (Git), and CI/CD for model deployment

Platform and MLOps - Architect and maintain ML infrastructure using cloud platforms (Databricks, AWS, Azure) and orchestration tools. - Implement CI/CD practices for model deployment, versioning, and automated retraining pipelines.

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Has Syngenta Group posted this job before?

Yes. We’ve followed 2 other Data Scientist jobs there since July 2026. 2 came down, usually after about 8 days.

São PauloCame down after 5 days, first seen Jul 5
São PauloCame down after 11 days, first seen Jul 10
This jobUp 34 days so far, first seen Oct 1

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025: $140,300
Expected change, 2025 to 2035: 21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Job Description

Position Summary:

Lead DS and ML predictive analytics solutions, owning technical strategy and delivery for data science projects of varying complexity.
Serve as technical lead for data science squads — driving architecture decisions, code quality standards, and delivery excellence across the team.
Bridge business and technical teams by translating complex analytical findings into clear, actionable insights that influence strategic decisions.
Foster a culture of continuous learning by mentoring data scientists, promoting best practices in MLOps, and championing innovation in analytical methods

Accountabilities:

Model Development & Analytics Leadership

Build and deploy predictive models and analytics capabilities, leading technical workstreams independently
Perform advanced data analysis and EDA to develop fact-based insights and strategic recommendations
Lead integration of traditional and nontraditional data sources into production analytical workflows
Drive integration of structured and unstructured data sources into robust, scalable analytical pipelines.

Platform and MLOps

Architect and maintain ML infrastructure using cloud platforms (Databricks, AWS, Azure) and orchestration tools.
Implement CI/CD practices for model deployment, versioning, and automated retraining pipelines.
Establish model monitoring, drift detection, and performance tracking frameworks in production.
Promote reusable components, shared libraries, and standardised development environments across the team.

Leadership & Stakeholder Management

Act as technical point of contact for stakeholders — translating business problems into clear data science problem statements.
Lead cross-functional working groups with business owners, data engineers, and domain experts.
Mentor and develop junior and mid-level data scientists through code reviews, knowledge sessions, and project coaching.
Facilitate agile ceremonies (sprint planning, retrospectives, technical demos) and hold the team accountable to delivery commitments.

Strategic Contribution

Identify and prioritise high-impact AI/ML opportunities across Commercial domains (Marketing, Sales, Pricing).
Build and share domain expertise, contributing to the development of team capability roadmaps.
Influence tooling and platform strategy decisions based on emerging best practices and business requirements.
Drive measurable business outcomes — framing model outputs in terms of ROI, efficiency gains, or revenue impact.

Qualifications

Bachelor's degree in Computer Science, Mathematics, Statistics, Data Science, or related quantitative field (Master's preferred)
3-5 years professional experience delivering production data science solutions
Proven track record leading analytical projects with measurable business impact
Advanced proficiency with Python ecosystem (scikit-learn, TensorFlow/PyTorch, pandas, numpy)
Strong SQL skills and experience with cloud data platforms (AWS, Azure, Databricks)
Experience with ML frameworks, version control (Git), and CI/CD for model deployment
Demonstrated leadership through mentoring, technical presentations, or cross-functional project ownership

Additional Information

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.

#LI-Hybrid

Company Description

At Syngenta Group, we're a global community of 56,000 innovators across 90 countries, united by a 250-year legacy of agricultural excellence. As the world's most local agricultural technology partner, we create tailor-made solutions that transform farming while protecting our planet, driven by our commitment to innovation, ethics, and integrity. Through our inclusive environment and diverse perspectives, we pioneer breakthrough solutions for farmers, society, and future generations. Join our worldwide teams of agricultural pioneers in creating a more resilient and equitable food system for all.

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