Data Scientist - R01571772

Lever, Inc.; Bengaluru; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Lever, Inc. is seeking a Data Scientist to design, develop, and deploy advanced ML models and forecasting solutions. You will work with Python, R, and modern ML frameworks to tackle complex business challenges.

The role involves rigorous statistical analysis, data quality validation, and collaboration with cross-functional teams to translate business needs into analytical solutions for production environments.

Qualifications

• Minimum 4 years in advanced data science and ML model development.
• Experience with large datasets and production model deployment.
• Strong statistical analysis capabilities and forecasting know-how.

Responsibilities

• Design, develop, and deploy statistical and ML models using Python, R and frameworks.
• Perform rigorous statistical analyses including hypothesis testing and regression.
• Implement data quality checks and ensure model integrity.
• Collaborate with cross-functional teams on data-driven strategies.
• Develop and maintain forecasting models using exponential smoothing, ARIMA, ARIMAX.
• Build, train, and evaluate classification and regression models (e.g., ENSEMBLE/SVM).
• Deploy and monitor models in production with cloud-native tools.
• Document methodologies and contribute to analytics best practices.

Skills

Python

PySpark

Statistical analysis

Regression techniques

Hypothesis testing

Probabilistic models

Classification algorithms

Forecasting techniques

R language

R Studio

Education

Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or related

Certification in Data Science or Machine Learning

Tools

Great Expectations

Evidently AI

TensorFlow

PyTorch

Sci-Kit Learn

Keras

MXNet

CNTK

KubeFlow

BentoML

R Studio

Job description

Data Scientist

Job requirements

Experience Range: With at least 4 years of experience in advanced data science, statistical analysis, and machine learning model development, including hands‑on work with large datasets and production model deployment. Key Responsibilities:

• Design, develop, and deploy advanced statistical and machine learning models using Python, R, and specialized frameworks to address complex business challenges
• Conduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling, to extract actionable insights from large-scale data
• Implement and validate data quality checks using tools such as Great Expectations and Evidently AI to ensure data and model integrity
• Collaborate with cross-functional teams to define data‑driven strategies, translate business requirements into analytical solutions, and present findings to stakeholders
• Develop, optimize, and maintain forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to support business planning
• Build, train, and evaluate classification and regression models using ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras, MXNet, CNTK)
• Deploy and monitor models in production environments using scalable cloud‑native tools such as KubeFlow and BentoML
• Document methodologies and contribute to continuous improvement of analytics best practices

Required Skills:

• Advanced proficiency in Python and PySpark for data analysis and model development
• Expertise in statistical analysis and computing using SAS or SPSS
• Hands‑on experience with regression techniques including linear and logistic regression
• Strong knowledge of hypothesis testing, including T-Test and Z-Test methodologies
• Proficient in building and interpreting probabilistic graphical models
• Experience with classification algorithms such as Decision Trees and Support Vector Machines (SVM)
• Skilled in forecasting techniques including exponential smoothing, ARIMA, and ARIMAX
• Familiarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance
• Working knowledge of R and R Studio for statistical modeling
• Experience with data validation and monitoring tools such as Great Expectations and Evidently AI

Preferred Skills:

• Experience deploying machine learning models using KubeFlow or BentoML
• Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK
• Background in cloud‑based analytics platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)
• Exposure to automated machine learning (AutoML) workflows

Desired Qualifications:

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
• Certification in Data Science or Machine Learning from a recognized provider (e.g., Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate)