New opportunity

Material+ - Senior Data Scientist - Artificial Intelligence

Srijan Technologies · Gurgaon/Gurugram, India

About this role

The Senior Data Scientist designs, prototypes and builds AI engines and services using machine learning, data mining and information retrieval. Working with business partners and cross-functional teams, the role defines technical problems and testable hypotheses, develops analytical models that reflect business decisions, and incorporates them into data products and tools.

Responsibilities

Identify ways to use data to improve digital marketing and product management decisions. Mine, transform and analyze CRM, digital analytics and external data; combine disparate datasets to create features.
Conduct advanced statistical analysis to identify trends, measure performance and produce actionable insights. Design, test and implement data models, use A/B testing to assess model quality, and integrate findings into business processes.
Coordinate model implementation with functional teams and develop processes and tools to monitor model performance and data accuracy. Collaborate with stakeholders, analysts and team members, including coaching and mentoring.

Requirements

An MS or PhD in Computer Science or Electronics, or an MS in Computer Applications, Economics, Statistics, Mathematics or a related technical discipline.
The detailed requirements specify 6+ years in a statistical, data science or data analyst role, although the listing header states 2–4 years.
Experience applying machine learning, statistics and data mining to clustering, classification and regression problems; experience merging and transforming internal and external datasets; and proficiency in R or Python.
Strong analytical, problem-solving, organizational and communication skills; attention to data validity, bias and accuracy; and willingness to work in Agile environments with multiple stakeholders.

Preferred

Experience with BI tools such as Tableau or Power BI is a plus. The position is listed in Gurgaon/Gurugram, India.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Data ScienceMachine LearningData MiningInformation RetrievalStatistical AnalysisData ModelingA/B TestingClusteringClassificationRegressionFeature EngineeringPythonRTableauPower BICRMDigital AnalyticsAgileCommunicationCollaborationMentoring

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Frame business decisions as testable problems
Work with business partners to identify digital marketing and product-management opportunities, define technical problems and hypotheses, and build models that reflect business decisions.
Turn disparate data into reliable features
Mine, merge and transform CRM, digital analytics, and external data; assess validity, bias and accuracy when creating features.
Build analytical models and AI services
Design, prototype, test and implement models, AI engines and services using machine learning, data mining, information retrieval and statistical analysis.
Measure quality and put insights to use
Use advanced analysis to identify trends and measure performance, apply A/B testing to assess model quality, and integrate insights into business processes.
Coordinate implementation and support others
Work across functional teams to implement models and monitor performance and data accuracy; coach and collaborate with business partners, analysts and team members.

Likely challenges to think through

Combining data without obscuring its weaknesses
A likely difficulty is gaining useful features from CRM, digital analytics and external sources without carrying forward mismatches or bias. Prepare to explain how you would check definitions, provenance and accuracy before trusting a merged dataset.
Balancing model quality with decision impact
A plausible trade-off is that a model can perform well on a technical metric yet fail to improve a marketing or product decision. Prepare to describe how you would choose business-relevant measures, form a testable hypothesis and interpret an A/B test before recommending adoption.
Sustaining performance after implementation
The posting calls for both cross-functional implementation and monitoring, so a likely challenge is balancing delivery with ongoing checks of model performance and data accuracy. Prepare an example approach for assigning monitoring responsibilities, detecting deterioration and communicating findings to stakeholders.

Practice your answers

Technical practice

How would you turn a digital marketing or product-management decision into a testable data science problem?

What to coverThe posting calls for defining technical problems and hypotheses with business partners. Suggested preparation: explain how you would identify the decision, target outcome, available data and criteria for a useful result.

How would you combine CRM, digital analytics and external data to create reliable features?

What to coverThe role involves merging and transforming disparate sources. Suggested preparation: discuss matching records, aligning time periods, handling missing or inconsistent values, and checking whether features would be available when a decision is made.

How would you use statistical analysis to identify a trend and turn it into an actionable insight?

What to coverThe posting asks for advanced analysis to identify trends and measure performance. Suggested preparation: describe how you would establish a baseline, examine uncertainty and possible confounders, then explain what decision the finding could support.

How would you choose among clustering, classification and regression for a business problem?

What to coverAll three problem types are named in the requirements. Suggested preparation: distinguish their outputs and assumptions, then explain how the business decision and available labels would guide your choice.

How would you evaluate an analytical model before incorporating it into a data product?

What to coverThe role includes designing and testing models that reflect business decisions. Suggested preparation: cover a decision-relevant baseline and metrics, appropriate validation, leakage checks, and trade-offs between predictive quality and usability.

How would you use an A/B test to assess whether a model improves a business decision?

What to coverThe posting explicitly calls for an A/B testing framework to test model quality and integrate insights into processes. Suggested preparation: explain the comparison, outcome measure, assignment approach and how you would interpret a result that differs from offline model metrics.

How would you prototype an AI service that uses information retrieval?

What to coverThe role summary names information retrieval and the design, prototyping and building of AI engines and services. Suggested preparation: describe a plausible retrieval use case, how you would assess result relevance, and what would determine whether the prototype is useful; no particular architecture is specified.

What would you check when a model's performance or input-data accuracy deteriorates after implementation?

What to coverThe responsibilities include developing processes and tools to monitor model performance and data accuracy. Suggested preparation: distinguish changes in input quality from changes in the relationship between inputs and outcomes, and explain how you would investigate each before recommending action.

How would you move an analytical model into a business process with functional teams?

What to coverThe posting calls for coordinating implementation and incorporating models into data products or tools. Suggested preparation: discuss the decision point, required inputs and outputs, ownership of checks, and trade-offs between model complexity and operational reliability.

How would you make an analysis in Python or R understandable to business stakeholders?

What to coverThe requirements name Python or R, strong communication, and BI tools such as Tableau or Power BI as a plus. Suggested preparation: explain how you would make calculations reproducible, show performance and limitations clearly, and choose a presentation suited to the decision rather than assuming a particular BI tool is required.

Behavioural practice

Tell me about a time you clarified an ambiguous request from a business partner.

What to coverThe role requires translating business needs into technical problems and testable hypotheses. Suggested preparation: use a real situation, your questions and actions, and the resulting decision or clearer scope.

Tell me about a time you coordinated with different teams to put an analytical result into use.

What to coverThe posting emphasizes cross-functional implementation and integration into business processes. Suggested preparation: describe the situation, how you handled differing needs or handoffs, and the result without overstating your individual contribution.

Tell me about a time you challenged a conclusion because the data might be inaccurate or biased.

What to coverThe posting calls for skepticism about data validity and bias, alongside attention to accuracy. Suggested preparation: explain the warning sign, the checks and communication you undertook, and what changed as a result.

Tell me about a time you coached or mentored someone on an analytical problem.

What to coverCoaching and mentoring analysts, team members and business partners are stated expectations. Suggested preparation: give a real situation, describe how you adapted your support, and explain the outcome for the work or the person.

Tell me about a time you balanced changing priorities across stakeholders in a fast-paced environment.

What to coverThe posting mentions Agile environments, multiple stakeholders and strong organization. Suggested preparation: set out the competing requests, how you prioritized and communicated trade-offs, and the result.

Sharpen your knowledge

Topics to prepare

Business problems and testable hypotheses
Rehearse turning a hypothetical digital-marketing or product-management decision into a technical problem, a testable hypothesis and a measure of success. The posting explicitly calls for defining problems with business partners and building models that reflect business decisions.
Clustering, classification and regression
For each problem type, explain when you would use it, what outcome it produces and how you would judge whether it helps a business decision. Experience applying machine learning, statistics and data mining to all three is explicitly requested.
Statistical analysis and A/B testing
Talk through a hypothetical model comparison: state the hypothesis, how you would compare outcomes and what would make the result actionable. The role calls for advanced statistical analysis and an A/B testing framework to assess model quality.
Data validity and bias
List plausible validity, bias and accuracy concerns when combining CRM, digital analytics and external data, then explain how each could distort a conclusion. The posting explicitly emphasizes skepticism about data validity and inherent biases.
Information retrieval in AI services
As a lower-priority review, sketch how an information-retrieval component could support an analytical service and how you would assess its usefulness. Information retrieval and building AI engines and services appear in the role summary; no particular retrieval approach is specified.

Skills to revise

Merge and transform data in R or Python
Using sample or synthetic CRM, digital-analytics and external datasets, join records, check mismatches and create a few documented features. R or Python and feature creation from disparate internal and external data are explicit requirements.
Prototype and evaluate a model
Build a small classification, regression or clustering prototype in R or Python; document the target or grouping objective, evaluation results and a limitation. The role calls for designing, testing and implementing analytical models for these problem types.
Draft an A/B test plan
For a hypothetical change to an analytical model, write the comparison, outcome measure, data checks and decision rule before looking at results. This practices the posting's responsibility to use A/B testing to assess model quality.
Design performance and accuracy checks
Create a short monitoring checklist for a hypothetical deployed model: what performance and input-data accuracy to review, and what would prompt investigation. Coordinating implementation and developing monitoring processes and tools are stated responsibilities.
Explain and coach across teams
Practice a brief explanation of a model's finding, uncertainty and proposed business action, then explain the same work to an analyst who will help maintain it. The posting emphasizes stakeholder communication, cross-functional collaboration and mentoring.

Your preparation checklist

Prepare a requirements evidence sheet
List examples of work in statistics, data science or analysis, along with your degree and R or Python experience. Note that the detailed posting says 6+ years while its header says 2–4 years; prepare to ask which applies.
Build a project walkthrough
Choose a relevant project and prepare a short account of the business decision, testable hypothesis, data sources, model, results and how findings were used. The role calls for analytical models incorporated into data products or tools.
Explain data preparation and validity checks
Prepare a diagram or brief example showing how you merged internal and external data, created features, and checked accuracy and potential bias. The posting specifically mentions CRM, digital analytics and external data.
Practice a model-design exercise
As a suggested exercise, frame a digital-marketing or product-management decision, then justify a clustering, classification or regression approach, its baseline and evaluation measures. Be ready to explain your choices in R or Python.
Draft an experiment and monitoring plan
Prepare a one-page example of how you would use A/B testing to assess model quality, then monitor performance and data accuracy after implementation. Distinguish proposed methods from work you have actually done.
Prepare collaboration and mentoring examples
Outline specific situations where you translated a partner's need into an analytics problem, coordinated implementation across teams, or coached an analyst. Include your contribution and the resulting decision or deliverable.
Write questions for the employer
Ask which business decisions the first projects would support, how models reach business processes, who owns ongoing monitoring, and how the team interprets the conflicting experience figures.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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