Data Scientist - TM

Aceolution India; Hyderabad; On-site; INR 2,400,000 - 4,000,000; Full time

Job summary

Aceolution India is seeking a highly skilled Data Scientist to join our growing team. The role focuses on data-driven problem solving, advanced statistical modeling, and Generative AI to transform data into predictive and prescriptive insights for strategic decisions.

As a senior leader, you will guide end-to-end data science projects, deploy AI solutions, communicate value to executives, and ensure robust, scalable models across business units.

Qualifications

• Bachelor's or Master's degree in a quantitative field.
• 6-10 years of progressive experience in Data Science, with senior or staff-level responsibilities.
• Strong proficiency in Python and ML libraries (PyTorch, TensorFlow, Scikit-learn).
• Experience with Generative AI, LLMs, and production systems.

Responsibilities

• Design, build, and optimize complex ML models, predictive algorithms, and statistical frameworks.
• End-to-end DS lifecycle from EDA to deployment and monitoring.
• Communicate findings and translate model outputs into actionable business recommendations.
• Collaborate with leaders to identify AI opportunities and define KPIs for model success.

Skills

Python

PyTorch

TensorFlow

Scikit-learn

LangGraph

A/B testing

Causal inference

Data analysis

Communication

presentation skills

Education

Bachelor's or Master's degree in a quantitative field

Tools

Spark

Job description

We are seeking a highly skilled and experienced Data Scientist to join our growing team. The ideal candidate will be a data-driven problem solver with a passion for uncovering insights that drive strategic business decisions through both advanced statistical modeling and cutting-edge Generative AI technologies. As a senior leader, the candidate will bridge the gap between complex algorithmic research and scalable business applications, ensuring our data assets are transformed into predictive and prescriptive intelligence.

Key Responsibilities

• Ability to design, build, and optimize complex machine learning models, predictive algorithms, and statistical frameworks.
• Handle end-to-end data science lifecycle: from exploratory data analysis and feature engineering to model deployment and monitoring.
• Identify and resolve algorithmic biases or performance bottlenecks, ensuring high model accuracy and robustness for production environments.

Generative AI & LLM Implementation:

• Develop and implement Generative AI solutions, including fine-tuning Large Language Models (LLMs) outputs and architecting RAG (Retrieval-Augmented Generation) systems to solve complex business problems.
• Explore and integrate advanced NLP techniques to automate data extraction and enhance decision-making capabilities across the organization.

Strategic Insights & Communication:

• Translate complex statistical findings and model outputs into clear, actionable business recommendations for stakeholders across different departments (e.g., Marketing, Product, Operations, Finance).
• Collaborate closely with business leaders to identify high-impact AI opportunities and define key performance indicators (KPIs) for model success.
• Prepare and deliver compelling presentations to senior leadership, distilling advanced technical methodology into strategic business value.
• Act as a subject matter expert on data science, machine learning, and Generative AI ethics for relevant business areas.

Qualifications

• Bachelor's or Master's degree in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Economics, Physics, or related discipline.
• [6-10] years of progressive experience in Data Science, with a significant portion in a senior or staff-level role and a proven track record of deploying Generative AI or LLM-integrated production systems.
• Strong proficiency in Python, along with deep expertise in machine learning libraries (e.g., PyTorch, TensorFlow, Scikit-learn), and Agentic Frameworks (e.g. LangGraph)
• Proven experience with advanced statistical and Timeseries modeling, experimental design (A/B testing), and causal inference.
• Experience with large datasets, distributed computing (e.g., Spark), and data warehousing concepts.
• Excellent analytical, problem-solving, and critical thinking skills, with the ability to navigate ambiguity in complex data environments.
• Strong communication and presentation skills, with the ability to explain complex algorithmic concepts to non-technical audiences.

Preferred Qualifications (Bonus Points)

• Prior experience working within Google and PLX environments.
• Experience with cloud platforms (GCP / Vertex AI) and MLOps frameworks.
• Knowledge of data governance, AI safety, and data security best practices.