Role

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 AND ROLE

Master's 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 Good m 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).

Solid 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., Tensor Flow, PyTorch) and architecture (e.g., neural networks, transformers, sequence modeling).

Experience with AI Agent frameworks (e.g., Google ADK, Lang Chain).

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).

For many transactions, IM has end-to-end visibility across the supply chain: from upstream manufacturers, through distribution, to resellers, to end users. This perspective provides opportunities to tackle the most challenging research and applied science questions with answers that drive strategic, operational, and tactical decisions with immediate and long-term relevance around the world.

ML Modeling and classification ,GCP Mandatory - Mandatory

📌 12493-Data Scientist - ING | CHN - 3days WFO (India)
🏢 Objectwin Technology India
📍 India