Data Scientist Neev System

Appsierra Group; Hyderabad; On-site; INR 2,550,000 - 3,450,000; Full time

Job summary

Appsierra Group is hiring a Data Scientist to lead advanced analytics for our digital ordering pipeline. You will define metrics, build predictive models, and create dashboards spanning order intake to delivery.

The role requires 8+ years of experience and a strong foundation in statistics, SQL, Python, and ML libraries. The position involves collaborating cross-functionally, working with AWS data services, and presenting complex insights to executives and engineering teams.

Qualifications

• 8+ years of experience in data science, ML engineering, or advanced analytics.
• Strong foundation in statistics including hypothesis testing, regression, time series, Bayesian methods.
• Advanced SQL across large, multi-source datasets.

Responsibilities

• Define and build metrics framework for digital ordering pipeline from intake to delivery.
• Design and deliver dashboards tracking order volume, throughput, turnaround times, error rates, system stability.

Skills

Statistics

SQL

Python

R

ML libraries

AWS

SageMaker

Tableau

Dashboarding

Communication

Education

Bachelor's or Master's in CS/IT

Tools

SageMaker

Redshift

Athena

Glue

QuickSight

Tableau

Job description

Job Requirement details

Job Title / Role Data Scientist

Type of Employment (Full time/Contract/Contract to hire) Full Time

Years of Experience Required (Min-Max) 8 + Years( Relevant should be 7 Years)

No. of Positions 2

CTC in INR 30 LPA

Bounty

Highest Qualification Required Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field. Work Location Hyderabad Mode of Work (Remote/On-site/Hybrid) WFO/4 Days Hybrid Mandatory

Notice Period/ Start Date Flexibility Immediate - 15 Days On-site feasibility (work abroad) No

Interview Rounds

L1- TechnicaL

L2 - Techno Mnagerial Client Interview

Interview Panel Time slots 12PM - 3PM - L1 Office Time 4:00 PM – 1:00 AM Weekend Drives No

Salaries paid on Last working day of the month

Must/Good to Have Details

Must have

• 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)

Key role and responsibilities

• 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 escalade
• 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