Prescriptive Analytics

Prescriptive Analytics Data Jobs

2 jobs found

3d ago

Senior Data Scientist, Cloud Gaming - Prescriptive Analytics and Optimization

NVIDIA Santa Clara, CA

Santa Clara, CA

3d ago

$184k - $288k/yr Full time Senior Analytics Big Data

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Job Description

This senior data scientist role focuses on prescriptive analytics and optimization for real-time cloud gaming, delivering scalable ML/AI and optimization solutions for routing, scheduling, and capacity management at NVIDIA.

Location

Santa Clara, CA (onsite)

Compensation

USD 184,000 - 287,500 per year

Experience and Education

Minimum 6 years of experience with a BS/MS or equivalent experience, or a PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or a related quantitative field.

Responsibilities

Develop and deploy scalable ML, AI, and optimization models to improve demand forecasting, optimize capacity allocation, and implement user-specific feature engineering for real-time cloud gaming services.
Create reusable framework deployments for data ingestion, processing, and analysis to support dynamic user interventions toward targeted business outcomes.
Apply domain knowledge of the product and software stack to identify data inconsistencies and enhance model performance, especially in the context of optimization results.
Analyze complex datasets to identify trends and patterns using supervised and unsupervised learning to inform prescriptive solutions.
Design and enhance real-time prescriptive scheduling pipelines using linear programming and constraint optimization to improve capacity utilization and user retention.
Increase organizational productivity by mining petabytes of data for actionable insights, often delivered as prescriptive recommendations.
Collaborate with a diverse set of partners to gather requirements, design robust solutions, and guide the team toward impactful results.
Leverage agentic AI to deliver advanced automation and programming solutions for complex analytical problems.

Requirements

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a focus on prescriptive analytics and optimization.
Strong foundation in probability, statistics, AI/ML, prescriptive modeling, and optimization methodologies such as linear programming, network flow, decision theory, and multi-armed bandit.
Proficient coding skills, primarily Python, with the ability to write readable, testable, maintainable, and extensible code; experience with optimization libraries such as Google OR-Tools.
Experience with data storage and processing tools and the ability to address issues in running large-scale software across substantial clusters.
Solid experience in data cleaning, aggregation, transformation, and extraction, with an understanding of how data quality affects performance.

Technologies

Python
SQL
Delta Lake
Apache Spark
Databricks
MLflow
Grafana
Elasticsearch
Google OR-Tools
Kubeflow

Benefits

Equity
Benefits

What you will be doing

Develop and deploy scalable ML, AI, and optimization models to improve demand forecasting, optimize capacity allocation, and craft user-specific features for real-time cloud gaming services.
Build reusable data ingestion, processing, and analysis frameworks to support dynamic user interventions for targeted business outcomes.
Apply product and software stack knowledge to resolve data inconsistencies and boost model performance within optimization contexts.
Identify and interpret patterns in complex data using supervised and unsupervised methods to inform prescriptive solutions.
Enhance real-time prescriptive scheduling pipelines through linear programming and constraint optimization to maximize capacity utilization and retention.
Drive organizational productivity by mining vast data stores for actionable insights and prescriptive guidance.
Collaborate with partners to define requirements, craft robust solutions, and lead the team toward meaningful results.
Utilize agentic AI to deliver automation and programming solutions for challenging analytical problems.

What we need to see

BS/MS (or equivalent experience) with 6+ years of experience or PhD in Data Science, Computer Science, Operations Research, Statistics, Applied Mathematics, or related quantitative fields, with a strong emphasis on prescriptive analytics and optimization.
Solid background in probability, statistics, AI/ML, prescriptive modeling, and optimization methods (eg, linear programming, network flow, decision theory, multi-armed bandit).
Strong coding ability in Python with experience in optimization libraries such as Google OR-Tools; ability to write clean, maintainable code.
Experience with data storage and processing tools and handling large-scale distributed software across clusters.
Proficiency in data cleaning, aggregation, transformation, and extraction, with awareness of data quality impacts on performance.

Ways to stand out from the crowd

Strong collaboration and presentation skills for working with multiple partners, with the ability to explain complex analytical solutions and their business implications.
Experience in time series analysis and forecasting for demand prediction in optimization contexts is advantageous.
Experience operating active ML production pipelines (MLflow, Kubeflow) with a focus on deploying and monitoring optimization models is advantageous.

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12d ago

Sr Data Scientist, Demand Planning

Intuit San Diego, CA

San Diego, CA

12d ago

$150k - $203k/yr Full time Analytics Big Data Business Intelligence

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Job Description

Intuit offers a benefits-forward culture with a cash bonus, equity rewards, and a comprehensive benefits package. This on-site role is based in San Diego, CA.

Location: San Diego, CA (onsite)

Salary: USD 149,500 - 202,500 per year

Overview

Intuit is seeking a Senior Data Scientist to own demand planning for the Full Service offering within the Consumer Group's Expert Network. You will be the primary modeler and analyst for forecasting Full Service Order volume, translating funnel signals into staffing inputs, and improving forecast accuracy across pre-season, in-season, and off-season horizons. This is an individual-contributor role with high visibility and cross-functional influence on workforce decisions.

What you will do

Lead end-to-end demand forecasting for Full Service, from early-season outlook to real-time in-season adjustments
Develop and maintain models that convert funnel signals (trade-up rates, FSO attach, offer acceptance) into actionable demand inputs for capacity planning
Produce interval-level, daily, and weekly forecasts that inform staffing and partner capacity across internal systems and external channels
Advance forecasting methodology by blending time-series models, regression approaches, and machine learning techniques to improve accuracy
Build scenario models with confidence intervals to quantify risk, including upside and downside demand cases for peak season
Explore and incorporate new signal sources such as marketing spend curves, product funnel data, historical tax filing trends, and macro indicators
Serve as the embedded data science partner for Workforce Management and Capacity Planning, translating outputs into staffing recommendations and operational levers
Collaborate with Finance on demand-to-revenue reconciliation and capacity cost modeling
Work with Marketing and Product on offer strategy and its downstream demand impact, including FSO trade-up promotions and LT offer windows
Support real-time in-season analytics, tracking WIP burndown, FSO funnel conversion, and coverage gap signals
Build and maintain dashboards and data products that surface demand risk to operational and leadership audiences
Contribute to post-season retrospectives on forecast accuracy, bias analysis, and methodology improvements
Write and maintain production-quality SQL and Python code against Intuit's datalake datasets related to capacity planning and FSO funnels
Partner with Data Engineering to improve upstream data quality and pipeline reliability for forecasting use cases
Document models, assumptions, and methodologies to enable reproducibility and stakeholder trust