Machine Learning Engineer / Data Scientist

Role & responsibilities

• Data Analysis: Analyze large, complex datasets to identify trends, patterns, and actionable insights relevant to marketing and business objectives.
• ML Model Development: Design, develop, and deploy machine learning models for predictive insights, automation, and decision support.
• Agentic AI Integration: Build and maintain data integrations to power AI and Agentic AI applications, ensuring seamless data flow and model performance.
• AI Tool Utilization: Actively use AI tools (Claude API, LangChain, or equivalent) to prototype, test, and deploy intelligent workflows.
• Data Visualization: Create compelling visualizations and reports to communicate findings to technical and non-technical stakeholders.
• Data Quality & Governance: Ensure accuracy, consistency, and reliability of data used for analysis and model development.
• Experimentation: Design and analyze A/B tests and experiments to evaluate strategy impact.
• Collaboration: Partner with marketing, product, and engineering teams to integrate analytical insights into business processes.

Preferred candidate profile

• Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field. Master's preferred.
• Portfolio demonstrating ML and analytics work (required).
• Ability to commute to or relocate to the GCC office location in India.

Qualifications

• Experience: 2+ years in data science or analysis with a portfolio demonstrating ML and statistical analysis.
• Programming: Strong proficiency in Python or R.
• SQL & Databases: Experience with SQL; familiarity with NoSQL a plus.
• ML Frameworks: Hands-on experience with scikit-learn, TensorFlow, and/or PyTorch.
• Visualization: Experience with Tableau, Power BI, Matplotlib, or Seaborn.
• Cloud Platforms: Familiarity with AWS, Azure, or GCP for data and ML services.
• AI/Agentic AI: Understanding of LLM architectures, prompt engineering, and agentic AI concepts.
• Communication: Ability to explain complex technical concepts to non-technical audiences.