Senior Data Analyst / Data Science Engineer (Python & Statistical Mode

Tekskills Inc.; Chennai; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Tekskills Inc. seeks a Senior Data Analyst / Data Science Engineer with 7+ years of Python-based analytics experience in Gurugram. You will convert complex Excel models into robust Python pipelines, validate parity, and deploy models with tests and documentation.

Strong regression and forecasting skills, plus data wrangling, are essential. You will collaborate with data engineering and BI teams to translate business questions into modelling solutions and to publish insights through dashboards

Qualifications

• 7–10 years in analytics/data science with Python proficiency.
• Strong regression techniques and model diagnostics experience.
• Experience converting Excel models into Python pipelines.
• SQL proficiency for data extraction and data quality control.
• Clear communication with stakeholders and requirement grooming.
• Version control (Git) and good notebook hygiene.

Responsibilities

• Migrate Excel-based models to Python with parity and performance gains.
• Design, build, and validate regression and related models.
• Perform data wrangling, EDA, feature engineering, and outlier handling.
• Document assumptions, diagnostics, and backtesting with metrics.
• Package models into versioned Python modules and pipelines with tests.
• Collaborate with data engineering and BI teams to publish results.

Skills

Python

Pandas

NumPy

Statsmodels

Scikit-learn

SQL

Git

Data wrangling

Tools

Airflow

Prefect

Docker

OpenPyXL/xlwings

Excel

Job description

Role: Senior Data Analyst / Data Science Engineer (Python & Statistical Modelling)

Location: Gurugram

Role Overview

Core mission: Own and productionize statistical models in Pythonprimarily regression and forecasting—by translating legacy Excel models into robust, tested, and scalable Python pipelines.

We’re looking for a Senior Data Analyst / Data Science Engineer with 7+ years of handson experience in Python-based analytics, statistical modelling (regression, forecasting, GLM), and strong data wrangling. You will convert complex Excel models into Python, validate parity, enhance them with software engineering best practices, and deploy them as reusable components/pipelines. The ideal candidate is equally comfortable discussing model assumptions with business stakeholders and implementing productionready code with tests and documentation.

Responsibilities

• Migrate existing Excel-based analytical/financial/statistical models into Python with feature parity, improved performance, and robust error handling.
• Design, build, and validate regression and related statistical models (e.g., Linear/Multiple Regression, Logistic, Elastic Net, Timeseries/ARIMA/Prophet, Panel data as applicable).
• Data wrangling & EDA: Clean, join, transform large datasets; exploratory analysis; feature engineering; handling outliers and missing data.
• Model governance: Document assumptions, diagnostics (VIF, residuals, multicollinearity, heteroskedasticity), backtesting, and performance metrics (RMSE/MAE/AUC as relevant).
• Productionization: Package models into versioned Python modules/notebooks/pipelines; write unit tests, data validation checks, and automation scripts (e.g., cron/Airflow).
• Stakeholder engagement: Translate business questions into modelling problems; communicate insights, tradeoffs, and explain model outcomes to nontechnical audiences.
• Collaboration: Partner with data engineering to access data; with BI teams to publish results (dashboards, APIs); with product/ops to integrate into decision flows.
• Optimization: Refactor legacy logic, improve runtime, and reduce manual effort through automation and reproducible analysis.
• Documentation & handover: Clear readme, change logs, model cards, and SOPs for ongoing maintenance.

Musthave qualifications

• 7–10 years in analytics/data science with demonstrable Python proficiency (pandas, numpy, statsmodels, scikitlearn).
• Strong command of regression techniques (linear, logistic, regularization, model diagnostics, crossvalidation).
• Proven experience converting Excel models into Python (e.g., complex formulas, lookups, macros/VBA logic translated to Pythonic pipelines).
• SQL proficiency for data extraction and joins; solid EDA and data quality practices.
• Clear, structured communication; stakeholder management and requirement grooming.
• Version control (Git), notebook hygiene, and documentation habits.

Nice to have

• Timeseries modelling (ARIMA/SARIMA/Prophet), survival analysis, or Bayesian methods.
• Workflow orchestration (Airflow/Prefect), CI/CD for analytics, basic Docker.
• Experience with Excel/VBA for model understanding; OpenPyXL/xlwings helpful.
• Cloud exposure (AWS/GCP/Azure) for data/compute; basic API integration.
• Visualization: matplotlib/seaborn/plotly; BI tools (Power BI/Tableau).
• Domain exposure to financial modelling, pricing, marketing analytics, risk, or operations (as applicable).