Responsibilities
Role: Data Scientist Type: Full-Time Level: Mid to Senior (3–5 Years of Experience)
About the Role We are hiring a Data Scientist to join our brand new "Data Lab" team within ISR (Information Security and Risk). You will turn complex data into actionable insights and intelligent models, working across the full data science lifecycle — from exploratory analysis and feature engineering to model deployment and monitoring — in a collaborative, fast-paced environment. This is a unique opportunity to be a founding member of a team applying data science at the heart of information security and risk.
- Analyze large, complex datasets to uncover patterns, trends, and business insights
- Build, train, and evaluate machine learning and statistical models
- Design and implement end-to-end ML pipelines from data ingestion to model serving
- Collaborate with engineering teams to deploy models into production
- Define KPIs and build dashboards to track model performance and data quality
- Conduct A/B testing and experimentation to validate hypotheses
- Work with stakeholders to translate business problems into data science solutions
- Document methodologies and present findings to technical and non-technical audiences
What We're Looking For A curious, rigorous thinker who is equally comfortable in a Jupyter notebook and a production codebase. You can independently drive analysis from raw data to deployed model, and communicate results clearly to both technical and business stakeholders.
Profile required
- 3–5 years of experience as a Data Scientist or in a similar analytical/ML role
- Strong proficiency in Python (pandas, NumPy, scikit-learn, matplotlib, seaborn)
- Experience with supervised and unsupervised learning algorithms
- Solid understanding of statistics: hypothesis testing, regression, distributions, Bayesian methods
- Experience with SQL and working with relational databases
- Familiarity with ML frameworks: TensorFlow, PyTorch, or XGBoost / LightGBM
- Experience with data visualization tools (Matplotlib, Plotly, Power BI, or Tableau)
- Understanding of model evaluation, cross-validation, and hyperparameter tuning
- Experience with MLflow, DVC, or similar experiment tracking tools
- Exposure to LLMs, fine-tuning, or GenAI use cases
- Familiarity with cloud ML platforms: Azure ML, AWS SageMaker, or Vertex AI
- Knowledge of feature stores and model registries
- Experience with PySpark or distributed computing for large-scale data
- Familiarity with RAG or embedding-based search systems
- Experience working in the cyber domain or having cyber knowledge will be an added advantage
Business insight