Data Scientist

Company Description

60,000

100

Purpose

• Be integral part of the P&S Data Science team that applies technical expertise in data management, data science, machine learning, artificial intelligence, and automation to design, build, deploy, and maintain solutions across multiple countries.
• Work with project teams and SMEs to understand business requirements and develop appropriate data and AI/ML solutions.
• Contribute solutions with explorative, predictive- or prescriptive models, utilizing optimization, simulation, and machine learning techniques to existing and new projects.
• Work independently to build applications for data collection and processing, exploration and visualization, analysis, regression, classification, and generation as required by the project and business teams.
  

Accountabilities

• Develop and implement complex statistical models, machine learning algorithms, and data mining techniques to extract insights from large datasets.
• Lead data science projects from conception to completion, defining scope, methodology, and deliverables.
• Collaborate with business leaders to translate data insights into actionable strategies and recommendations.
• Guide and mentor junior data scientists, fostering their professional development and technical skills.
• Contribute to the design and improvement of data architecture, pipelines, and storage solutions.
• Stay current with the latest advancements in data science and introduce new techniques or technologies to the organization.
• Establish and maintain standards for data quality, documentation, and ethical use of data.
• Present complex findings to both technical and non-technical audiences, including executive leadership.
• Address complex business challenges using data-driven approaches and creative solutions.
• Improve the efficiency and scalability of data processing and model deployment.
• Develop deep knowledge in Syngenta’s P&S operations to better contextualize data insights.
• Own the design, build, and deploy process including collaboration with users and multi-disciplinary teams to fulfil the user, business and technical requirements.
  

Qualifications

Required Knowledge & Technical Skills

• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related discipline (or equivalent practical experience).
• Understanding of ETL processes and data pipeline design.
• Required development skills:
• Data Science – TensorFlow Scikit-learn, SciPy, NumPy, Pandas, XGBoost, Keras, etc.
• Programming – Python with pytorch and tensorflow
• Data – SQL, EDA, Descriptive and Predictive analysis, visualization
• Preferred additional skills:
• Web services – RESTful APIs and API testing, JSON, etc.
• Front end – JavaScript, Flask, React, Node.js, Vue, Django, or other.
• Tools – Git, npm, pip, Heroku, or other tools.
• Knowledge and hands-on experience supervised and unsupervised ML using Logistic/multi-variate regression, Gradient Boosting, Decision Trees, Neural Network, Random Forest, Support Vector Machine, Naive Bayes, Time Series, Optimization, etc.
• Preference for proven experience in adapting algorithms to required models - Regression, Decision Trees, Random Forests, LLM.
• Preference for experience in production and supply domain: production planning, supply chain, logistics, track and trace, CRM, etc.
• Preference for experience in Deep Learning model development in agriculture, supply chain or related domains.
• Documentation of documentation of APIs, models, and operational manuals (Markdown, etc.).
• Must be able to work on end to end activities from design, development and deployment
  

Required Experience

• Previous internship, placement, or project experience in data engineering, data science, software development, or a related field.
• Required 2 years experience of building data science projects using AI/ML models.
• Exposure to cloud platforms such as AWS, Azure, Google Cloud, or Data Bricks (preferred but not essential).
  

Additional Information

India page