Data Scientist – TM

Aceolution; Hyderabad; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Aceolution is seeking a Data Scientist – TM to bridge advanced research with scalable business applications. You will lead end-to-end data science initiatives, implement Generative AI and LLM solutions, and collaborate with stakeholders to drive strategic AI opportunities.

The role emphasizes production-ready ML models, bias mitigation, and performance monitoring, with a focus on practical business impact and governance in AI systems.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Physics, or a related quantitative discipline.
• 6–10 years of progressive experience in Data Science, with senior or staff-level experience.
• Proven experience developing and deploying Generative AI or LLM-integrated production systems.
• Strong proficiency in Python and ML libraries (PyTorch, TensorFlow, Scikit-learn).
• Hands-on experience with Agentic AI frameworks, such as LangGraph.
• Experience with large datasets and distributed computing (Spark) and data warehousing concepts.

Responsibilities

• Design, build, optimize, and deploy complex machine learning models, predictive algorithms, and statistical models.
• Manage the end-to-end data science lifecycle, including exploratory data analysis, feature engineering, model development, deployment, and monitoring.
• Identify and resolve algorithmic bias, model performance issues, and production bottlenecks.
• Ensure models are accurate, robust, scalable, and suitable for production environments.
• Develop and implement Generative AI solutions addressing complex business problems.
• Work with Large Language Models (LLMs) including prompt engineering, output optimization, and fine-tuning.
• Design and implement RAG (Retrieval-Augmented Generation) solutions.
• Explore and integrate advanced NLP techniques for data extraction, automation, and decision-making.
• Work with Agentic AI frameworks, such as LangGraph, to build AI workflows and applications.
• Translate statistical analysis and model outputs into actionable business recommendations.
• Collaborate with business leaders to identify high-impact AI and Data Science opportunities.
• Define KPIs and success metrics for AI/ML models and solutions.
• Prepare and present technical findings to senior leadership and cross-functional stakeholders.
• Act as a subject matter expert in Data Science, ML, Generative AI, and AI ethics.

Skills

Statistical modeling

Time-series modeling

A/B testing

Causal inference

Communication

Education

Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Physics

Tools

Python

PyTorch

TensorFlow

Scikit-learn

LangGraph

Spark

Job description

We are seeking a highly skilled and experienced Data Scientist – TM to join our growing team. The ideal candidate will be a data-driven problem solver with strong expertise in machine learning, statistical modeling, Generative AI, and Large Language Models (LLMs).

The candidate will be responsible for bridging advanced algorithmic research with scalable business applications and transforming data into predictive and prescriptive intelligence. This role will involve end-to-end data science, Generative AI implementation, stakeholder collaboration, and providing strategic insights to support business decisions.

Key Responsibilities

• Design, build, optimize, and deploy complex machine learning models, predictive algorithms, and statistical models.
• Manage the end-to-end data science lifecycle, including exploratory data analysis, feature engineering, model development, deployment, and monitoring.
• Identify and resolve algorithmic bias, model performance issues, and production bottlenecks.
• Ensure models are accurate, robust, scalable, and suitable for production environments.

Generative AI & LLM Implementation

• Develop and implement Generative AI solutions to address complex business problems.
• Work with Large Language Models (LLMs), including prompt engineering, output optimization, and fine-tuning where applicable.
• Design and implement RAG (Retrieval-Augmented Generation) solutions.
• Explore and integrate advanced NLP techniques for data extraction, automation, and decision-making.
• Work with Agentic AI frameworks, such as LangGraph, to build intelligent AI workflows and applications.

Strategic Insights & Communication

• Translate complex statistical analysis and model outputs into clear and actionable business recommendations.
• Collaborate with business leaders to identify high-impact AI and Data Science opportunities.
• Define KPIs and success metrics for AI/ML models and solutions.
• Prepare and present technical findings and recommendations to senior leadership and cross-functional stakeholders.
• Act as a subject matter expert in Data Science, Machine Learning, Generative AI, and AI ethics.

Qualifications

• Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Physics, or a related quantitative discipline.
• 6–10 years of progressive experience in Data Science, with significant experience in a senior or staff-level role.
• Proven experience developing and deploying Generative AI or LLM-integrated production systems.
• Strong proficiency in Python and machine learning libraries such as PyTorch, TensorFlow, and Scikit-learn.
• Hands-on experience with Agentic AI frameworks, such as LangGraph.
• Strong knowledge of statistical modeling, time-series modeling, experimental design/A-B testing, and causal inference.
• Experience working with large datasets, distributed computing technologies such as Spark, and data warehousing concepts.
• Strong analytical, problem-solving, and critical-thinking skills.
• Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical audiences.

Preferred Qualifications

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