The Role
Build and ship production machine learning and data-processing systems for intelligent payroll. Responsibilities include developing PySpark pipelines, forecasting and anomaly-detection models, production Python modules, cloud ML jobs, subsystem contracts, tests, documentation, and code reviews. The role focuses on Azure-based batch ML pipelines and scalable payroll data processing, with strong emphasis on software engineering practices, data quality, orchestration, and maintainable architecture.
Summary Generated by Built In
Machine Learning Engineer — Intelligent Payroll (TPY)
Team: HC Forward — Intelligent Payroll Role type: Individual contributor, builder track (no client engagement) Experience: 8–10 years hands-on building production data/ML systems Openings: 2
Human Capital — HC Forward
The Human Capital Offering Portfolio helps organizations manage and sustain their performance through their most important asset: their people. We see Human Capital as a topic critical to the C-Suite, and we bring together technology, insights, and services to help our clients innovate faster and proactively drive their business strategies.
HC Forward is Deloitte’s innovation engine for Human Capital — integrating technology, data, and industry expertise to create scalable solutions and assets that extend client capabilities and drive ongoing value across all Human Capital offerings.
About Intelligent Payroll
Intelligent Payroll is an HC Forward platform that uses machine learning to detect anomalies, forecast variances, and reduce manual intervention in enterprise payroll processing. It runs on Azure, ingests data from multiple HRIS sources (SAP, Oracle, ADP, Workday), normalises it into a common data model, and serves both batch ML pipelines and rule-based validation engines.
This is a builder role. You will spend your time writing production Python code, designing pipelines, and shipping features. You will not be in client workshops, executive presentations, or pre-sales.
Data Scientist, 5-8 years experience,
Work you’ll do
∙Design and ship distributed data processing pipelines in PySpark, moving raw payroll data through a medallion architecture (raw → standardised → aggregated → modelled)
∙Build ML pipelines for time-series forecasting and anomaly detection over payroll data
∙Write clean, class-based, testable Python — interfaces, factories, dependency injection, not 800-line procedural scripts
∙Submit and orchestrate Spark jobs programmatically on a managed cloud ML platform (we use Azure ML Studio, but the patterns generalise)
∙Own the contracts between subsystems: data pipelines, ML libraries, and orchestration layers are intentionally decoupled, and you will help keep them that way
∙Write tests, write docs, review PRs, and treat the pipeline as a product, not a notebook
Requirements
Required Skills
These are non-negotiable. If you don’t have these, this isn’t the right role.
Production PySpark
∙You have shipped PySpark code that ran in production — not just one notebook, but a pipeline that processed real data on a recurring schedule.
∙You know the DataFrame API, schema validation, parquet I/O, partitioning, and how to debug a job that’s slow or wrong.
∙You can write a PySpark job that runs locally for development and at scale in the cloud, without changing the business logic.
Strong Python & OOP
∙You write production Python by default: classes, modules, type hints, tests.
∙You know when to reach for an abstract base class, a factory, or dependency injection — and when not to.
∙You can read someone else’s class-based codebase and explain what it does, end to end.
Engineering experience
∙You design for boundaries: schemas between steps, interfaces between modules, contracts between services.
∙You ask about data quality before you ask about model choice.
∙You can work from a config file, a CLI entry point, and a YAML pipeline definition — not just python script.py.
Cloud ML platforms
∙You have submitted Spark or ML jobs programmatically using a cloud ML SDK (Azure ML, AWS SageMaker / boto3, Vertex AI) and understand cloud environments.
Data science fundamentals
∙Hands-on with core ML algorithms, with a solid grounding in regression.
∙Time-series modelling is a plus, not required.
Desired Skills
You don’t need all of these. You don’t even need most. But each one is a real signal.
Area
What it looks like
Cloud ML platforms
Comfortable working with cloud ML SDKs (Azure ML, AWS
SageMaker / boto3, Vertex AI) and submitting Spark or ML jobs
programmatically; understands cloud environments
Azure identity model
You can explain DefaultAzureCredential, managed identity vs
Skills Required
- Production experience building and operating recurring PySpark pipelines using the DataFrame API, schema validation, Parquet I/O, partitioning, and performance debugging.
- Strong production Python and object-oriented programming skills, including classes, modules, type hints, interfaces, factories, dependency injection, and testing.
- Experience designing system boundaries, data schemas, module interfaces, service contracts, configuration files, CLI entry points, and YAML pipeline definitions.
- Experience submitting Spark or machine learning jobs programmatically through a cloud ML SDK, such as Azure ML, AWS SageMaker/boto3, or Vertex AI.
- Hands-on knowledge of core machine learning algorithms and regression.
- Five to eight years of experience as a data scientist or in a related role.
- Eight to ten years of hands-on experience building production data and machine learning systems.
- Time-series modeling experience.
- Comfort working with cloud ML SDKs and cloud environments.
- Knowledge of Azure identity concepts, including DefaultAzureCredential and managed identity.
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The Company
What We Do
Alignity is a Talent Solutions company focused on revolutionizing talent acquisition, employer branding, and performance inspiration to help organizations achieve accelerated growth.
