Job Description
- As a Data Scientist, you’ll play a key role in turning strategic visions and roadmaps into real-world AI solutions - ranging from intelligent automation and predictive models to recommendation engines. This role sits at the heart of applied data science, with a strong focus on discovery, experimentation, and delivering business-ready outcomes.
- This is a hands-on technical role for someone who thrives on solving complex problems with data. As an individual contributor, you’ll work within a collaborative team environment that values autonomy, ownership, and technical excellence.
- You’ll partner closely with business stakeholders, product owners, and IT teams to design, build, and deploy machine learning solutions that drive measurable impact and enable smarter decision-making across the organization.
Key Responsibilities
- Develop, validate, and deploy machine learning and statistical models
- Collaborate with product managers, business SMEs, Software Engineers, Solution Architects, and Product/Program Managers to translate use cases into data-driven solutions
- Prepare and process structured and unstructured datasets for analysis and model training
- Evaluate model performance and continuously improve based on feedback and data drift
- Document methodologies and present findings clearly to both technical and non-technical stakeholders
- Support handover and integration of solutions into production environments
- Demonstrating curiosity in the latest and greatest tech trends.
- 1–5 years of hands-on experience in data science, with a proven track record of delivering successful AI/ML projects
- Demonstrated ability to bring AI/ML solutions to life end-to-end: from scoping and design to development, testing, deployment, and post-launch monitoring
- Experience building and deploying cloud-based ML solutions at scale, ideally using Docker and Kubernetes
- Strong foundation in advanced statistical modeling and data analysis
- Solid grounding in software engineering principles, DevOps practices, and MLOps deployment pipelines
- Effective communicator with the ability to explain complex technical topics to both technical and non-technical audiences
Skills
- Proficient in Python and key machine learning libraries (e.g., scikit-learn, pandas, NumPy, PyTorch/TensorFlow)
- Experience with ML experiment tracking and collaboration tools such as MLflow or Weights & Biases
- Familiarity with pipeline orchestration tools like Airflow, Kubeflow, or Argo
- Exposure to generative AI frameworks and SDKs (e.g., Langchain, Semantic Kernel, RAG), and tools like MS Co-Pilot Studio, ML Studio, Prompt Flow, Kedro, etc.
- Knowledge of infrastructure and operational concerns in deploying ML models to production environments
- Results-oriented mindset with strong problem-solving skills and a passion for innovation
- Highly organized, self-starter, able to manage multiple priorities and think strategically about opportunities to apply AI
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