Data Science Engineer

Data Science Engineer | Hiringhood Phygital Matchday- Hyderabad

About the Opportunity

Hiringhood is conducting a Phygital Matchday hiring drive for Data Science Engineer opportunities across 2 GCCs and 3 Indian product companies.

The Phygital hiring model combines online preliminary screening with mandatory in-person assessments for shortlisted candidates who qualify in the online round.

We are looking for Data Science Engineers who can transform data into predictive models, experiments, and actionable business insights, and take those solutions into production.

Role Focus: Approximately 70% hands-on Data Science (analysis, modelling, and experimentation) and 30% engineering (deployment, monitoring, and maintenance).

Key Responsibilities

• Problem Framing: Translate business questions into data science problems with clear hypotheses, target variables, and success metrics.
• Exploratory Data Analysis: Analyse distributions, missing values, outliers, seasonality, and potential bias.
• Feature Engineering: Develop and document features from structured, time-series, and text data.
• Machine Learning: Build and tune supervised and unsupervised models, including regression, gradient boosting, clustering, forecasting, and anomaly detection.
• Model Evaluation: Apply cross-validation, time-based splits, holdout testing, and appropriate evaluation metrics. Identify data leakage and overfitting.
• Experimentation: Design and analyse A/B tests using statistical tests, power analysis, and causal inference techniques.
• Model Interpretability: Explain model behaviour using feature importance, SHAP, partial dependence, visualisations, and business recommendations.
• Productionisation: Deploy models as services or scheduled jobs, monitor accuracy and data drift, and implement retraining where required.
• LLM Applications: Use LLMs for suitable use cases such as text classification, information extraction, and summarisation.

Required Qualifications

• 4-8 years of experience in Data Science or Applied Machine Learning.
• Experience building at least one production ML model with measurable business impact.
• Strong statistical foundations, including probability, hypothesis testing, confidence intervals, regression, and Bayesian basics.
• Hands-on experience designing and analysing A/B tests, including sample size, statistical power, and common experimentation pitfalls.
• Strong ML knowledge using Scikit-learn and XGBoost or LightGBM.
• Understanding of biasvariance trade-offs, regularisation, and hyperparameter tuning.
• Experience with time-series forecasting techniques such as ARIMA, Prophet, or gradient boosting with lag features.
• Strong Python skills using Pandas or Polars, NumPy, and Statsmodels.
• Advanced SQL skills for working with large datasets.
• Ability to explain model behaviour and uncertainty to non-technical stakeholders.
• Experience with data visualisation tools such as Matplotlib, Seaborn, or Plotly.
• Working knowledge of Git, Docker, and at least one cloud platform: AWS, GCP, or Azure.
• Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field, or equivalent experience.

Nice to Have

• Causal inference techniques such as Difference-in-Differences, propensity scoring, uplift modelling, or synthetic control.
• Deep learning using PyTorch or TensorFlow, or NLP with transformer models.
• Experience evaluating LLM outputs or using LLMs for classification and extraction.
• Knowledge of recommendation systems, optimisation, or survival analysis.
• MLOps experience with MLflow, feature stores, model monitoring, and drift detection.
• Domain experience in the relevant industry or a similar sector.
• Kaggle achievements, research publications, or open-source contributions in Data Science.

Hiring Process Phygital Matchday

Stage 1: Online Registration & Preliminary Assessment

1. Register for the Data Science Engineer opportunity through Hiringhood.
2. Select your preferred in-person Matchday date during registration:
   
• Saturday, 24 October 2026
• Sunday, 25 October 2026
3. Complete the online preliminary multiple-choice (MCQ) assessment.
4. Only candidates who qualify in the preliminary MCQ will be eligible to attend the in-person Matchday.

Important: Registration and date selection alone do not confirm eligibility for the in-person assessment.

Stage 2: In-Person Matchday Assessments

Candidates who qualify in the online preliminary assessment must attend in person on their selected date and complete all three rounds.

Round 1 Technical MCQ Assessment

Topics include statistics, probability, hypothesis testing, A/B testing, Python, SQL, feature engineering, ML algorithms, model validation, and time-series forecasting.

Round 2 – Coding Assessment

Hands-on Python and SQL problem-solving, data manipulation, modelling logic, and practical Data Science implementation.

Round 3 – TARA AI Technical Interview

An AI-powered technical interview covering Data Science projects, modelling decisions, business impact, evaluation methods, experimentation, productionisation, communication, and problem-solving.

Stage 3: Employer Evaluation & Shortlisting

Assessment results and candidate profiles will be evaluated against each participating employer's specific hiring criteria.

Employers may apply different requirements based on assessment scores, relevant experience, technical expertise, industry knowledge, and overall role fit.

Candidates who meet an employer's criteria may be shortlisted for the next stage of that employer's selection process.

Please Note: Completing all assessments does not guarantee shortlisting, an interview, or a job offer. Final selection is subject to each employer's eligibility and hiring criteria.

Important Instructions for Candidates

• In-person attendance is mandatory for candidates who qualify in the preliminary online MCQ.
• Select either 24 October or 25 October 2026 during registration.
• Complete all three in-person rounds: Technical MCQ, Coding Assessment, and TARA AI Technical Interview.
• Final shortlisting and selection decisions will be made by the participating employers.

Why Participate?

• One structured Matchday to demonstrate your Data Science and ML capabilities.
• An opportunity to be considered for relevant roles across 2 GCCs and 3 Indian product companies.
• A structured assessment process covering technical knowledge, practical coding, and technical communication.
• Potential access to further employer selection processes, subject to individual hiring criteria.

Take the next step in your Data Science career with Hiringhood's Phygital Matchday!