AI Data Scientist Industrial AI

Introduction

Job Title: AI Data Scientist (Industrial AI)

Department: SymphonyAI Industrial

Overview:

SymphonyAI is at the forefront of innovation, leveraging cutting-edge artificial intelligence and machine learning technologies to transform industries and drive business growth. As a global leader in AI-powered solutions, we empower organizations to harness the full potential of data-driven insights. SymphonyAI enterprise applications rapidly deliver transformative business value across retail, CPG, financial services, manufacturing, media, Enterprise IT (SymphonyAI Summit), and the public sector. SymphonyAI combines unrivalled AI technology, vertical expertise, and industry-specific data and insights into applications that drive the highest value for customers. We are one of the largest and fastest growing AI portfolios, on a mission to build a “World Class Engineering Team” with a high-performance culture.

Job Description

As AI Data Scientist, you will be responsible for designing, building, and deploying AI/ML solutions for industrial use cases. You will work closely with product, engineering, and client stakeholders to translate complex business problems into scalable data science solutions that deliver measurable impact.

This role requires a balance of strong technical expertise, practical problem-solving, and the ability to operate in real-world production environments—not just experimentation.

Responsibilities:

• End-to-End Model Development: Build, validate, and deploy machine learning models for industrial applications such as predictive maintenance, anomaly detection, forecasting, and optimization.
• Problem Framing & Data Analysis: Work with stakeholders to understand business problems, define success metrics, and perform deep data analysis to identify solution approaches.
• Production Deployment: Collaborate with engineering teams to productionize models, ensuring scalability, reliability, and performance.
• Client Interaction: Engage with client stakeholders to explain approaches, interpret results, and ensure alignment on business outcomes.
• Model Monitoring & Improvement: Track model performance post-deployment and continuously improve accuracy, robustness, and business impact.
• Cross-functional Collaboration: Partner with product managers, domain experts, and engineers to deliver integrated AI solutions.
• Mentorship: Support and guide junior team members; contribute to best practices and knowledge sharing.
  
  

• 5-8 years of experience in Data Science, Machine Learning, or Applied AI roles.
• Strong hands-on experience in Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Proven experience deploying ML models into production environments.
• Solid understanding of statistics, machine learning algorithms, and data processing techniques.
• Experience working with large, real-world datasets (structured and/or time-series).
• Ability to translate business problems into analytical solutions.
• Strong communication skills with experience working with non-technical stakeholders.
  
  

• Experience in industrial/manufacturing, energy, or supply chain domains.
• Familiarity with predictive maintenance, asset performance, or IoT data.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Exposure to MLOps tools and practices (CI/CD, model monitoring, pipelines).
• Background in SaaS or consulting environments with client-facing exposure.