Data Scientist

Fincons Group is looking for an experienced AI / Machine Learning Engineer to support data-driven initiatives across complex business and operational environments. This role focuses on identifying high-impact AI/ML use cases, developing and deploying machine learning solutions, and working closely with cross-functional stakeholders to turn data into actionable insights. The ideal candidate brings a strong technical foundation, a problem-solving mindset, and the ability to communicate effectively with both technical and non-technical teams.

• Identify AI/ML Opportunities: Partner with cross-functional business and technical teams to identify and define AI and machine learning use cases that improve efficiency, performance, and decision-making. Help shape project scope, objectives, and success metrics.

• End-to-End ML Delivery: Own machine learning projects throughout their lifecycle, including data collection, preprocessing, model development, deployment, and ongoing monitoring in production environments.

• Programming & Data Engineering: Develop clean, maintainable code (primarily in Python) to build data pipelines, implement ML models, and automate analytical workflows. Use SQL for data extraction and transformation.

• Data Analysis & Modeling: Analyze large, complex datasets from operational systems (e.g., ERP, MES, IoT, or similar sources) to identify trends, engineer features, and build predictive or prescriptive models.

• Stakeholder Communication: Translate technical findings into clear, actionable insights for non-technical stakeholders to support data-driven decisions.

• Experience:
  
  3+ years of hands-on experience in AI, Machine Learning, or Data Science. Experience in industrial, manufacturing, or operational environments is a plus.

• Technical
  
  Skills:
  
  

  • Strong proficiency in Python and common ML libraries (e.g., scikit-learn, Tensor Flow, PyTorch)
  • Solid SQL skills for querying and data manipulation
  • Experience with data visualization tools (e.g., Power BI, Tableau, or similar)
  • Basic understanding of MLOps concepts, including model deployment and monitoring

• Problem-Solving:
  Demonstrated ability to break down complex problems and deliver practical, data-driven solutions.

• Communication &
  
  Collaboration:
  
  Strong communication skills with the ability to work effectively across technical and non-technical teams.

• Experience with cloud platforms such as AWS, Azure, or GCP
• Exposure to time-series analysis, anomaly detection, optimization techniques, or similar advanced analytics
• Familiarity with operational or manufacturing data and processes (e.g., quality, maintenance, supply chain)