Data Scientist

Hiringhood; Hyderabad; On-site; INR 2,600,000 - 4,200,000; Full time

Job summary

Hiringhood is seeking a Data Scientist to build the analytics and data science foundation for an ordering platform. You will define metrics, build dashboards, and layer predictive models and anomaly detection to make the end-to-end process visible and self-correcting.

This greenfield role requires 8+ years in data science or ML, strong statistics, SQL, Python/R, and experience with AWS, Tableau, and production-grade analytics.

Qualifications

• 8+ years of experience in data science, ML engineering, or advanced analytics.
• Strong foundation in statistics and hypothesis testing, regression, time series.
• Advanced SQL across large, multi-source datasets.
• Proficiency in Python or R for analysis and modeling.
• Experience with ML libraries such as scikit-learn, statsmodels, pandas, NumPy.
• Experience with AWS data and ML services (SageMaker, Redshift, Athena, Glue, QuickSight).
• Hands-on experience with Tableau for dashboards.
• Ability to define metrics frameworks and build dashboards from scratch.
• Experience building anomaly detection or predictive models in production.
• Strong communication to present findings to executives and engineers.

Responsibilities

• Define and build the metrics framework for the digital ordering pipeline from order intake through result delivery.
• Design and deliver dashboards tracking order volume, throughput, turnaround times, error rates, and system stability.
• Build predictive models to forecast failures, volume trends, and capacity needs.
• Develop automated anomaly detection to surface pipeline issues early.
• Apply statistical methods for root cause analysis to diagnose why systems fail.
• Instrument data collection with engineering teams and ensure data gaps are addressed.
• Translate complex findings into clear narratives for leadership and engineering teams.
• Investigate ad-hoc data questions and support root-cause analysis, quantifying incident impact.
• Document metric definitions, model logic, data sources, and dashboard design for sustainability.

Job description

About the Role

We're looking for a Data Scientist to build the analytics and data science foundation for ordering platform. Orders flow in from multiple channels, move through lab systems and integrations, and results flow back out to clinicians you'll make that entire journey visible, predictable, and self-correcting. Your job is to define the metrics, build the dashboards, and layer on predictive models and anomaly detection that help the engineering organization move from reactive to proactive.

This is a greenfield effort. You'll deliver a self-sustaining analytics and data science layer that the engineering organization can operate going forward.

What You'll Do

• Define and build the metrics framework for the digital ordering pipeline from order intake through result delivery
• Design and deliver dashboards that track order volume, throughput, turnaround times, error rates, and system stability across multiple integration points
• Build predictive models to forecast order failures, volume trends, and capacity needs
• Develop automated anomaly detection to surface pipeline issues before they escalations
• Apply statistical methods for root cause analysis diagnosing why systems fail, not just what failed
• Partner with engineering teams to instrument data collection where gaps exist
• Translate complex technical and statistical findings into clear narratives for executive leadership, engineering management, and individual engineering teams
• Investigate ad-hoc data questions diagnosing production issues, quantifying impact of incidents, and supporting root cause analysis
• Document metric definitions, model logic, data sources, and dashboard design so the organization can maintain and extend your work independently

Required

• 8+ years of experience in a data scientist, ML engineer, or advanced analytics role
• Strong foundation in statistics - hypothesis testing, regression, time series analysis, Bayesian methods
• Advanced SQL - comfortable writing complex queries across large, multi-source datasets
• Proficiency in Python or R for analysis, modeling, and automation
• Experience with ML/statistical libraries (scikit-learn, statsmodels, pandas, NumPy, or similar)
• Experience with AWS data and ML services (SageMaker, Redshift, Athena, Glue, QuickSight, or similar)
• Hands-on experience with Tableau
• Demonstrated ability to define metrics frameworks and build dashboards from scratch, not just maintain existing ones
• Experience building anomaly detection or predictive models in a production or operational context
• Strong communication skills - able to present statistical findings to executives, engineering leaders, and technical teams with equal clarity
• Experience working across multiple teams or systems, synthesizing data from disparate sources into a unified view

Good to Have

• Familiarity with healthcare, diagnostics, or lab operations
• Experience with operational analytics (error tracking, SLA monitoring, system health metrics)
• Experience with real-time or streaming analytics (Kinesis, Lambda)