Data Analytics Trainer

Einstro; Chengalpattu; On-site; INR 551,000 - 1,102,000; Part time

Job summary

Einstro is seeking a part-time Data Analytics/Data Science Trainer to independently deliver the analytics segment of an integrated AI/ML/Data program. The trainer will bridge cohorts from Python fundamentals into data analysis and foundational machine learning, before advancing learners to deep learning, generative AI and RAG topics.

Ideal candidates hold a degree in CS/IT/DS/Statistics/Math/Engineering and have 2+ years of analytics or technical-training experience.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Statistics, Mathematics, Engineering or a related discipline.
• Two or more years of analytics, data-science, technical-training or industry experience preferred.
• Experience training college students, graduates or working professionals is an advantage.
• Practical project experience with realistic datasets is strongly preferred.

Responsibilities

• Deliver the analytics segment of an integrated AI/ML/Data program.
• Bridge the cohort from Python fundamentals into data analysis and foundational ML.
• Introduce learners to data preparation, EDA, visualization and basic ML pipelines.
• Guide learners through advanced modules such as deep learning, generative AI and RAG concepts.

Skills

SQL

Statistics

Python for analytics

Descriptive analytics

Exploratory data analysis

Visualization

Power BI

Foundational ML (scikit-learn)

Education

Bachelor's or Master's in CS/IT/DS/Stats/Math/Engineering

Tools

Power BI

Power Query

DAX

Job description

Role & responsibilities

We are seeking a part-time Data Analytics Trainer or Data Science Trainer who can independently deliver the analytics segment of an integrated AI, Machine Learning and Data program. The trainer will bridge the cohort from completed Python fundamentals into data analysis and foundational machine learning, before learners progress to advanced modules such as deep learning, generative AI, large language models, retrieval-augmented generation and agentic AI.

• SQL: relational concepts, data types, filtering, aggregation, joins, subqueries, common table expressions, window functions and query-based analysis.
• Statistics: descriptive statistics, probability fundamentals, distributions, sampling, confidence intervals, hypothesis testing, correlation and regression concepts.
• Python for analytics: NumPy fundamentals, Pandas Series and DataFrames, indexing, transformation, grouping, merging and time-series basics.
• Data preparation: missing values, duplicates, outliers, inconsistent formats, feature quality and practical data-cleaning decisions.
• Exploratory data analysis: univariate and multivariate analysis, pattern discovery, visualisation and evidence-based interpretation.
• Visualisation: Matplotlib and Seaborn fundamentals, chart selection, clarity, labelling and responsible interpretation.
• Power BI: Power Query, data modelling, relationships, calculated columns, measures, DAX fundamentals, dashboard design and publishing concepts.
• Foundational machine learning where assigned: supervised and unsupervised learning, preprocessing, train-test split, common algorithms, evaluation metrics, overfitting and introductory pipelines using scikit-learn.

Preferred candidate profile

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Statistics, Mathematics, Engineering or a related discipline.
• Two or more years of relevant analytics, data-science, technical-training or industry experience preferred.
• Previous experience training college students, graduates or working professionals will be an advantage.
• Practical project experience using real or realistic datasets is strongly preferred.
• Foundational machine-learning capability using scikit-learn will be a significant advantage.
• Relevant certifications in Power BI, data analytics, SQL, Python, statistics or data science will be valued but will not replace demonstrated ability.
• Strong candidates with less formal teaching experience may be considered if they demonstrate excellent technical depth and instructional ability.