Senior Data Scientist

7 days ago

Bengaluru, Karnataka, India Acronotics Limited Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Job Description: AI Data Scientist

Brief

We are seeking a Data Scientist to lead the predictive analytics and forecasting in our projects The candidate will design, build, and deploy machine learning models that predict financial KPIs, simulate market scenarios, and generate forward-looking insights by merging internal financial and external market data sources. Top candidates will also be excellent leaders and communicators.

This role involves working with Azure AI Foundry, OpenAI, RAG pipelines, and enterprise datasets (Structured like Power BI and unstructured like Legal contracts) to deliver solutions.

Responsibilities

· Develop and implement forecasting and simulation models for financial KPIs (e.g., EBITDA, revenue, cost drivers, FX exposure).

· Integrate and pre process structured (Power BI datasets, OLAP cubes) and semi-structured (FastMarkets, Excel, PPTs) data.

· Design what-if analysis frameworks to assess the impact of external market changes (e.g., commodity prices, FX rates, carbon credit prices).

· Work closely with AI engineers to embed predictive models into the Copilot interface (web and Teams).

· Evaluate and fine-tune model performance using appropriate metrics and business validation.

· Collaborate with financial analysts and business SMEs to interpret predictions and ensure model explainability.

· Document model architecture, data pipelines, and assumptions for audit and reproducibility.

Requirements

· 4–6 years of experience as a Data Scientist, ideally in financial analytics, forecasting, or risk modelling.

· Bachelor's degree in Data Science, Statistics, Computer Science, or a related quantitative field.

· Strong command of Python (NumPy, Pandas, scikit-learn, statsmodels, PyTorch or TensorFlow).

· Proven experience with time-series forecasting models (ARIMA, Prophet, LSTM, regression-based forecasting).

· Experience working with Power BI datasets and SQL/OLAP data sources.

· Proficiency in data cleaning, feature engineering, and model evaluation for numeric KPIs.

· Experience in Azure ecosystem (Azure Machine Learning, Azure AI Foundry, Data Lake, or Synapse) preferred.

· Experience in Azure ML pipeline deployment.

· Understanding of financial KPIs, statements, and variance analysis (preferred from manufacturing or commodities domain).

· Familiarity with LLM-integrated analytics (RAG, Azure OpenAI) is a plus.

  • · Excellent communication and documentation skills; ability to explain technical results to business users.

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