Job Description :

We are seeking an experienced Data Scientist II to drive business impact through rigorous data analysis, statistical modeling, and machine learning.

The role will collaborate with a diverse team of highly talented data professionals, engineers, and domain experts to transform complex datasets into actionable insights and high-performing predictive models.

You will focus on exploring granular data, formulating statistical hypotheses, training robust machine learning models, and delivering business-critical insights.

As a Data Scientist, you will build and validate models, execute deep-dive data analyses, communicate findings to key internal stakeholders, and own sub-streams of projects requiring effective cross-functional collaboration.

Qualifications :

- Masters degree (or equivalent technical degree) in Statistics, Computer Science, Data Science, Applied Mathematics, Economics, Engineering, or a related quantitative field.

- 5+ years of quantitative industry experience solving complex business problems using data science methods.

- 3+ years in ML modeling and classification.

- 3+ years of strong, hands-on experience with Python and advanced SQL, including experience with data modeling and query optimization for complex analytical workloads.

- Deep knowledge of statistical concepts (e.g., hypothesis testing, regression analysis, experimental design, probability distributions) and core machine learning techniques (supervised/unsupervised learning, tree-based models, time-series analysis).

- Hands-on proficiency with Python data science libraries (e.g., pandas, numpy, scikit-learn, etc.).

- Strong proficiency in data visualization and business intelligence - translating granular, complex data into intuitive dashboards and stories (e.g., Power BI, Tableau, Matplotlib, Seaborn).

- Experience cleaning, aggregating, and pre-processing large datasets from varied relational and cloud databases.

- Experience working in cloud environments (GCP, AWS, or Azure).

- Strong problem-solving abilities with a proven track record of applying statistical rigor and ML techniques to real-world business challenges.

- Excellent verbal and written communication skills; ability to articulate technical concepts and statistical findings to both technical peers and non-technical business partners.

Nice to Have :

- Experience with Deep Learning (DL) frameworks (e.g., TensorFlow, PyTorch) and architecture (e.g., neural networks, transformers, sequence modeling).

- Experience with AI Agent frameworks (e.g., Google ADK, LangChain).

- Familiarity with MLOps best practices, model deployment pipelines, model monitoring, or containerization (Docker, Kubernetes).

- Exposure to production data engineering tools and pipeline orchestration (e.g., Airflow).

Core Focus :

- We prioritize DS-specific skill sets: Regression, Classification, Forecasting, Other predictive modelling skills along with Python, GCP. RAG and Agentic work are secondary skills.