Data Scientist
Data Scientist
ascendionThis job is no longer accepting applications
Job Description
Job Responsibilities
Build and maintain mathematical optimization models — objective functions,
constraints, and penalty calibration
Encode and validate business rules, contract parameters, and operational
constraints into model inputs
Execute regression testing and model health checks before and after every
production run
Conduct scenario and sensitivity analysis with documented trade-offs and
business recommendations
Validate all input files pre-run — NULL checks, format errors, anomalies, and
data readiness sign-off
Monitor data pipeline health, audit logs, and ingestion failures across
recurring run cycles
Perform data quality assessments — missingness, outliers, duplicate records,
and consistency checks
Validate model outputs and savings calculations — ensuring mathematical
accuracy and business defensibility
Review compliance and performance tracking — compare actuals vs recommendations
and document deviations
Review output files for anomalies and formatting issues before stakeholder
delivery
Support recurring run preparation — scenario setup, input file review, and run
orchestration
Triage platform issues — classify severity, gather diagnostics, and escalate
with clear context
Monitor cloud pipeline health — ingestion, compute, and delivery layer checks
Maintain run logs, SOPs, runbooks, and knowledge base in structured auditable
format
Partner with business stakeholders to explain model outputs and recommendations
in plain language
Mentor junior team members on best practices, coding standards, and
documentation discipline
Required Skills
3–8+ years experience in Data Science / Operations Research.
Hands-on experience with optimization solvers — CPLEX and OR-Tools.(Operation
Research Tools)
Python — modeling, data validation, automation scripting, production-quality
coding
SQL — data querying, pipeline monitoring, audit log review
Cloud platform experience — AWS or equivalent
Strong documentation and stakeholder communication skills
Preferred Skills
Pipeline orchestration tools — Dataiku, Airflow, or equivalent
Containerization — Docker and Kubernetes
REST API development — FastAPI or equivalent
Open-source solver experience — PuLP, OR-Tools
Domain knowledge in logistics, supply chain, or network optimization
Cloud certification — AWS
Build and maintain mathematical optimization models — objective functions,
constraints, and penalty calibration
Encode and validate business rules, contract parameters, and operational
constraints into model inputs
Execute regression testing and model health checks before and after every
production run
Conduct scenario and sensitivity analysis with documented trade-offs and
business recommendations
Validate all input files pre-run — NULL checks, format errors, anomalies, and
data readiness sign-off
Monitor data pipeline health, audit logs, and ingestion failures across
recurring run cycles
Perform data quality assessments — missingness, outliers, duplicate records,
and consistency checks
Validate model outputs and savings calculations — ensuring mathematical
accuracy and business defensibility
Review compliance and performance tracking — compare actuals vs recommendations
and document deviations
Review output files for anomalies and formatting issues before stakeholder
delivery
Support recurring run preparation — scenario setup, input file review, and run
orchestration
Triage platform issues — classify severity, gather diagnostics, and escalate
with clear context
Monitor cloud pipeline health — ingestion, compute, and delivery layer checks
Maintain run logs, SOPs, runbooks, and knowledge base in structured auditable
format
Partner with business stakeholders to explain model outputs and recommendations
in plain language
Mentor junior team members on best practices, coding standards, and
documentation discipline
Required Skills
3–8+ years experience in Data Science / Operations Research.
Hands-on experience with optimization solvers — CPLEX and OR-Tools.(Operation
Research Tools)
Python — modeling, data validation, automation scripting, production-quality
coding
SQL — data querying, pipeline monitoring, audit log review
Cloud platform experience — AWS or equivalent
Strong documentation and stakeholder communication skills
Preferred Skills
Pipeline orchestration tools — Dataiku, Airflow, or equivalent
Containerization — Docker and Kubernetes
REST API development — FastAPI or equivalent
Open-source solver experience — PuLP, OR-Tools
Domain knowledge in logistics, supply chain, or network optimization
Cloud certification — AWS
More Info
Job Type:
Industry:
Function:
Employment Type:
About Company
Skills:
Predictive Modeling, Machine Learning, Amazon Web Services, Artificial Intelligence, Natural Language Processing, Data Mining, Apache Spark, Big Data, Deep Learning, Data Science, Algorithms, R Programming Language, Data Visualization, Statistics, Python Programming Language, Tableau Business Intelligence Software, SQL Programming Language, Data Analysis, Statistical Modeling
Skills:
Java, Unix, Sql, Hive, shell scripting, Linux, MySQL, Data Visualization, Oracle, Python, Teradata, R
Skills:
causal analysis , Machine Learning, Statistical Inference, Hypothesis Testing, Tableau, Python, Sql, experimentation, Statistics
Skills:
Natural Language Understanding (NLU), Automatic Speech Recognition (ASR), Computer Vision (CV), Machine Learning, Hadoop, SAS, Sql, Business Intelligence Tools, Hive, Spss, Spark, Matlab, Python, Statistical packages, Map-reduce, R, S-PLUS
Skills:
Logistic Regression, Cluster Analysis, Predictive Modelling, Random Forest, Python, Multivariate regression models, R, Ada-Boost, Statistical Machine Learning, Statistical tools and techniques