Data Science Associate

Neoqubit Pvt Ltd.; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Neoqubit Pvt Ltd. in Bengaluru is seeking a Data Science Associate Consultant with 4-6 years of hands-on experience in statistical modeling, machine learning, and modern AI techniques.

You will translate business problems into data science solutions, build and evaluate models, and work with both structured and unstructured data to drive AI/LLM initiatives. The role emphasizes vector embeddings, semantic layers, metadata, ontologies, and knowledge representation, with a strong emphasis on

Qualifications

• 4-6 years of professional data science experience.
• Strong foundations in statistics and probability.
• Hands-on with regression, classification, clustering, and ensemble methods.
• Experience with Python and ML libraries.
• Familiarity with LLMs, embeddings, RAG, and semantic modeling.

Responsibilities

• Apply statistical methods to business problems and derive actionable insights.
• Develop, validate, and deploy ML models for real-world use cases.
• Work with structured and unstructured data; build semantic representations.
• Design prompts, embeddings, and retrieval workflows for AI-driven solutions.
• Communicate findings with clear visualizations and business implications.

Skills

Statistical modeling

Machine learning

LLMs

Prompt engineering

Data storytelling

Education

Bachelor's or Master's in CS/Math/Statistics

Tools

Python

Pandas

Scikit-learn

XGBoost/LightGBM

Job description

We are looking for a Data Science Associate Consultant with 4-6 years of hands-on experience in statistical modeling, machine learning, and modern AI techniques. The ideal candidate should have strong fundamentals in statistics and machine learning, combined with practical exposure to LLMs, prompting, embeddings, RAG, semantic layers, metadata, ontologies, taxonomies, and knowledge representation. The role requires someone who can translate business problems into data science solutions, build and evaluate machine learning models, work with structured and unstructured data, and contribute to AI/LLM-driven solutions involving semantic understanding and relationships between business entities.

Responsibilities:

• Apply strong statistical foundations to solve business and analytical problems.
• Work extensively with probability distributions, descriptive and inferential statistics, hypothesis testing, correlation and covariance, sampling techniques, statistical significance, and confidence intervals.
• Develop and interpret statistical models to identify trends, relationships, patterns, and drivers within data.
• Select appropriate statistical techniques based on the problem, data characteristics, and business objective.
• Validate model assumptions and assess statistical reliability of results.
• Design, develop, train, validate, and deploy machine learning models for real-world business problems.
• Demonstrate strong hands-on experience across regression, classification, clustering, ensemble methods, feature engineering, and model selection and validation.
• Build regression models such as linear, logistic, regularized, and other appropriate approaches.
• Work with classification algorithms and evaluate models using metrics such as precision, recall, F1-score, ROC-AUC, and confusion matrix.
• Apply clustering techniques to identify meaningful segments and patterns.
• Use ensemble approaches such as Random Forest, Gradient Boosting, XGBoost, LightGBM, or similar techniques.
• Perform feature selection, transformation, encoding, scaling, dimensionality reduction, and other feature engineering techniques.
• Identify data leakage, overfitting, underfitting, bias, and other modeling issues.
• Perform hyperparameter tuning and cross-validation to improve model performance.
• Perform comprehensive exploratory data analysis to understand datasets and identify meaningful patterns.
• Identify missing values, outliers, inconsistencies, data quality issues, and distribution characteristics.
• Develop meaningful features from raw business data.
• Communicate analytical findings through clear visualizations, statistical summaries, and business insights.
• Work with structured and semi-structured datasets from multiple sources.
• Develop a strong understanding of large language models and their application in enterprise use cases.
• Work with prompt engineering, prompt optimization, context management, embeddings, semantic similarity, retrieval-augmented generation, and LLM evaluation.
• Design prompts for analytical, information retrieval, classification, summarization, and reasoning use cases.
• Work with embedding models and understand how embeddings can be used for semantic search and retrieval.
• Contribute to RAG-based solutions by understanding document ingestion, chunking, embeddings, retrieval, ranking, and response generation.
• Evaluate LLM outputs for relevance, accuracy, consistency, and grounding.
• Understand limitations of LLM-based systems such as hallucination, context limitations, and non-deterministic responses.
• Work with semantic layers that provide a business-oriented representation of data and metrics.
• Develop an understanding of metadata, ontologies, taxonomies, entities, attributes, relationships, business concepts, and semantic mappings.
• Analyze and structure relationships between business entities and concepts.
• Help define standardized business terminology and semantic relationships across disparate datasets.
• Work with metadata to improve data discoverability, interpretation, governance, and AI-driven retrieval.
• Understand how semantic models can support analytics, AI applications, search, and decision-making.
• Translate complex business concepts into structured representations that can be consumed by data and AI systems.
• Understand how entities and relationships can be represented using graph structures.
• Work with nodes, edges, graph traversal, entity resolution, relationship mapping, and graph-based search and reasoning.
• Contribute to knowledge graph or semantic modeling initiatives where applicable.
• Understand how graph-based representations can complement traditional databases, search systems, and LLM/RAG architectures.
• Translate ambiguous business problems into well-defined data science problems.
• Identify appropriate statistical, machine learning, or AI approaches based on the business requirement.
• Collaborate with business stakeholders, data engineers, software engineers, product teams, and other consultants.
• Present analytical findings and model outcomes in a clear and business-friendly manner.
• Explain complex data science concepts to technical and non-technical stakeholders.
• Balance model accuracy, interpretability, scalability, and business impact.
• Define appropriate evaluation metrics based on the business problem.
• Perform model validation and benchmarking.
• Monitor model performance and identify opportunities for improvement.
• Compare multiple modeling approaches and recommend the most suitable solution.
• Document assumptions, methodologies, experiments, results, and limitations.
• Follow reproducible and well-documented data science practices.

Requirements:

• 4-6 years of professional experience in data science, machine learning, advanced analytics, or a related field.
• Strong foundation in statistics and probability.
• Strong hands-on experience with regression, classification, clustering, ensemble methods, and feature engineering.
• Strong programming experience in Python.
• Hands-on experience with data science and machine learning libraries such as Pandas, NumPy, Scikit-learn, and XGBoost or LightGBM.
• Strong understanding of model evaluation, validation, feature engineering, and statistical analysis.
• Working knowledge of LLMs and generative AI.
• Practical understanding of prompt engineering, embeddings, semantic search, and RAG.
• Understanding of semantic layers, metadata, ontology, taxonomy, entities, and relationships.
• Strong analytical and problem-solving skills.
• Ability to communicate technical concepts clearly to business and technical stakeholders.
• Bachelor\'s or master\'s degree in computer science, data science, statistics, mathematics, engineering, economics, or a related quantitative discipline.
• An advanced degree in statistics, data science, machine learning, or a related field is a plus.

Good To Have:

• Experience working with knowledge graphs or graph-based data models.
• Exposure to graph databases such as Neo4j or similar technologies.
• Experience with vector databases and semantic retrieval.
• Exposure to LLM frameworks and orchestration tools.
• Experience with NLP and text analytics.
• Knowledge of entity resolution, entity linking, or relationship extraction.
• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Exposure to MLOps, model deployment, monitoring, and CI/CD.
• Experience with SQL and relational databases.
• Experience working with large-scale datasets and distributed data processing.
• Experience in enterprise consulting or client-facing data science engagements.