Machine Learning and Statistical Analysis

Job Details:

Experience: Minimum 5+ years of relevant industry experience in statistics, data science, or advanced analytics

Education:Master's degree in Statistics (B.Stat/ M.Stat /B.Sc. Stats/ M.Sc. Statistics /B.Sc. Maths/ M.Sc. Maths or equivalent).

Required Skills: Python,Machine Learning Algorithms (Ex. Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGboost, GBM etc), MS-Excel, Data Model/Pivot Tables in MS Excel, MS Powerpoint, Good Knowledge of Statistics, Statistical Tests & Distributions, Experience in Data Analysis, Data Preparation & Model building

Location: Bangalor

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Role Overvie

w:Develop end to end solutions including understanding the business need, data mining, build and validate predictive modelling using the Machine Language approaches, hypothesis testing and generate actionable business insights from data analysi

s.The candidate will work closely with business stakeholders to translate analytical insights into actionable, high‑impact solutions, with a strong focus on predictive modelling, risk analytics, and decision scienc

e.Communicate the model results & insights and plan out the communication strategy in consultation with business using A/B techniques and Design of Experiment

s.Track the model effectiveness and feed the learnings to fine tune the model

s.
Job Responsibilitie

s:
Machine Learning and Statistical Anal

• ysis
  Apply advanced statistical techniques (regression, hypothesis testing, multivariate analysis, survival analysis, time series modelling) to solve business pro
• blemsDesign and validate predictive and explanatory models using structured and unstructured
• dataPerform model diagnostics, assumptions testing, and robustness c
• hecksDevelop statistically sound sampling frameworks, experiment designs (A/B testing), and inference me
• thodsBuild and optimize machine learning models such as XGBoost, Random Forests, Logistic Regression, GBMs, Neural Networks,
• etc.Focus on business‑driven model optimization (e.g., recall vs precision trade‑offs, cost‑sensitive lear
• ning)Perform feature engineering, variable selection, and model t
• uningWork with large datasets using Python / SQL, ensuring data quality and consis
• tencyCollaborate with data engineering teams for data pipelines, model deployment, and autom
• ationModel Training, Deployment, Pipeline building and alerts using AWS Cloud - s3, ec2, ECR, ECS/Fargate, Lamda, step function, cloud watch, SES