Science,IT & Telecoms,IT & Telecoms|Data Scientist,Science|Scientist,Engineering Jobs

Role : Senior Data Scientist / AI Engineer

Location : Remote

Salary: $120k to $125k

IMP: - Skills - Agentic AI (A2A , MCP) , Azure Cloud , healthcare -payer space experience.

Exp : 10+

Job description:-

We're looking for a few Senior Data Scientist / AI Engineer with deep expertise in advanced Machine Learning, Large Language Models (LLMs) and Knowledge Graphs,.

This role is ideal for someone who enjoys solving complex problems, building intelligent systems end to end, and driving measurable impact in data rich environments.

What You'll Do:

- Design advanced ML models across NLP, optimization, predictive modeling, and statistical learning.

- Own end to end MLOps pipelines: data ingestion, training, deployment, monitoring, CI/CD.

- Collaborate with engineering, product, and domain teams to deliver production ready AI solutions.

- Build and scale Knowledge Graph–driven AI systems (ontology design, graph embeddings, reasoning).

- Develop and fine tune LLMs for classification, summarization, RAG, and agentic workflows.

What We're Looking For:

- PhD (preferred) or Master's in CS, AI, ML, Data Science, or related fields.

Strong hands on experience with:

- Core ML & Stats (optimization, supervised/unsupervised learning)

- NLP (semantic search, embeddings, text modeling)

- MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD)

- Proficiency in Python, PyTorch/TensorFlow, HuggingFace, LangChain, and cloud platforms.

- Ability to translate complex ideas into scalable, real world systems.

- Knowledge Graphs (RDF/OWL, Neo4j, graph ML)

- LLMs & Transformers (fine tuning, RAG, prompt engineering)

Preferred

- Healthcare insurance / Managed Care (MCO) experience — familiarity with claims, clinical workflows, risk models, or regulatory frameworks is a strong plus.

- Experience with vector databases, hybrid semantic neural architectures, or agentic AI systems.

- Background in explainable or responsible AI.

Senior Data Scientist/ML Engineer
Location: Middletown, NJ (Onsite from Day 1)
Long Term
Job Summary:
Strong academic foundation in Statistics (Master s degree or higher preferred).
Hands-on, real-world experience working with complex and messy datasets with minimal direction.
Ability to explore data with a clear project goal or overarching business question in mind.
Experience with advanced statistical and econometric models, including:
Constrained hierarchical GLMs with weighted partial pooling.
Bayesian hierarchical models with weighted partial pooling.
Constrained random coefficient models.
Proven experience working with large-scale datasets in cloud-based environments (e.g., Databricks)
Familiarity with code version control systems (e.g., GitHub) to enable faster onboarding

Indent ID: SF_OP_(phone number removed)

Client Full Address: Mountain view, CA

Remote / WFO / Hybrid: Onsite

Hire Type (FTE/Contract): Contract

No. of client interviews for this role: 2

Rate : 62

Key Responsibilities

• SQL Development & Optimization: Write, maintain, and optimize complex SQL queries using CTEs, window functions, joins, and aggregations.

• Data Engineering & Pipelines: Build, schedule, monitor, and maintain reliable data pipelines for accurate and consistent reporting.

• Data Modeling & Table Design: Design analytical tables with appropriate partitions, keys, and levels of granularity.

• Data Investigation: Trace metrics to source systems, identify root causes, and reconcile discrepancies between reports.

• Reporting & Semantic Layer: Support measures, dimensions, aggregations, and consistent reporting across applications.

• Data Governance: Navigate table-access requests and handle restricted data appropriately.

• Collaboration & Documentation: Partner with UI developers and stakeholders; document datasets and use Git, pull requests, and code reviews.

Required Qualifications

• Strong advanced SQL skills, including CTEs, window functions, complex joins, and aggregations.

• Solid data engineering experience with pipelines, transformations, scheduling, and data quality.

• Experience designing analytical tables, including partitioning, keys, and data granularity.

• Ability to investigate data issues, reconcile reports, and perform root-cause analysis.

• Experience working with large datasets and analytical data platforms.

• Familiarity with access permissions, data governance, and restricted datasets.

• Working knowledge of Git, pull requests, and collaborative development.

• Basic Python scripting for automation, data manipulation, or troubleshooting.

• Strong communication skills and clear technical documentation for UI and engineering teams.

Preferred Qualifications

• Cube (Cube.dev) experience is a significant plus: defining measures and dimensions in YAML; understanding

rollups and pre-aggregations; troubleshooting when queries do not use pre-aggregated data.

• Hands-on experience with Databricks and Spark SQL.

2

• Familiarity with call center/contact center data and reporting is a plus, including call volume, average handle time

(AHT), service levels, agent performance, and customer interactions.

• Experience with operational dashboards, KPI reporting, and performance analytics, ideally in a call center or customer

support setting.

• Familiarity with semantic data modeling and analytics infrastructure.

• Experience working closely with frontend/UI engineering teams.

What Success Looks Like

• Reliable datasets and pipelines that support downstream reporting and applications.

• Efficient SQL queries and scalable data models.

• Independent investigation and clear explanation of reporting discrepancies.

• Consistent metrics across data sources and reporting layers.

• Clear documentation for UI developers and other data consumers.

• Improved reporting performance and maintainability.

Ideal Candidate Profile

We seek a technically strong, detail-oriented problem solver comfortable at the intersection of SQL development, data

engineering, and analytics. The ideal candidate can independently investigate complex data issues, understand data

architecture, and build dependable solutions. While Cube and call center reporting familiarity are highly valued, deep

SQL expertise and strong data engineering or data investigation experience are the highest priorities.

ndent : PSL(phone number removed)

Role : Senior Data Scientist / AI Engineer

Location : Dayton, Ohio (Hybrid)

Salary: $120k to $125k

IMP: - Skills - Agentic AI (A2A , MCP) , Azure Cloud , healthcare -payer space experience.

Exp : 10+

Job description:-

We're looking for a few Senior Data Scientist / AI Engineer with deep expertise in advanced Machine Learning, Large Language Models (LLMs) and Knowledge Graphs,.

This role is ideal for someone who enjoys solving complex problems, building intelligent systems end to end, and driving measurable impact in data rich environments.

What You'll Do:

- Design advanced ML models across NLP, optimization, predictive modeling, and statistical learning.

- Own end to end MLOps pipelines: data ingestion, training, deployment, monitoring, CI/CD.

- Collaborate with engineering, product, and domain teams to deliver production ready AI solutions.

- Build and scale Knowledge Graph–driven AI systems (ontology design, graph embeddings, reasoning).

- Develop and fine tune LLMs for classification, summarization, RAG, and agentic workflows.

What We're Looking For:

- PhD (preferred) or Master's in CS, AI, ML, Data Science, or related fields.

Strong hands on experience with:

- Core ML & Stats (optimization, supervised/unsupervised learning)

- NLP (semantic search, embeddings, text modeling)

- MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD)

- Proficiency in Python, PyTorch/TensorFlow, HuggingFace, LangChain, and cloud platforms.

- Ability to translate complex ideas into scalable, real world systems.

- Knowledge Graphs (RDF/OWL, Neo4j, graph ML)

- LLMs & Transformers (fine tuning, RAG, prompt engineering)

Preferred

- Healthcare insurance / Managed Care (MCO) experience — familiarity with claims, clinical workflows, risk models, or regulatory frameworks is a strong plus.

- Experience with vector databases, hybrid semantic neural architectures, or agentic AI systems.

- Background in explainable or responsible AI.

Indent : PSL(phone number removed)

Role : Senior Data Scientist / AI Engineer

Location : Remote

Salary: $120k to $125k

IMP: - Skills - Agentic AI (A2A , MCP) , Azure Cloud , healthcare -payer space experience.

Exp : 10+

Job description:-

We're looking for a few Senior Data Scientist / AI Engineer with deep expertise in advanced Machine Learning, Large Language Models (LLMs) and Knowledge Graphs,.

This role is ideal for someone who enjoys solving complex problems, building intelligent systems end to end, and driving measurable impact in data rich environments.

What You'll Do:

- Design advanced ML models across NLP, optimization, predictive modeling, and statistical learning.

- Own end to end MLOps pipelines: data ingestion, training, deployment, monitoring, CI/CD.

- Collaborate with engineering, product, and domain teams to deliver production ready AI solutions.

- Build and scale Knowledge Graph–driven AI systems (ontology design, graph embeddings, reasoning).

- Develop and fine tune LLMs for classification, summarization, RAG, and agentic workflows.

What We're Looking For:

- PhD (preferred) or Master's in CS, AI, ML, Data Science, or related fields.

Strong hands on experience with:

- Core ML & Stats (optimization, supervised/unsupervised learning)

- NLP (semantic search, embeddings, text modeling)

- MLOps (MLflow, Kubeflow, Airflow, Docker, CI/CD)

- Proficiency in Python, PyTorch/TensorFlow, HuggingFace, LangChain, and cloud platforms.

- Ability to translate complex ideas into scalable, real world systems.

- Knowledge Graphs (RDF/OWL, Neo4j, graph ML)

- LLMs & Transformers (fine tuning, RAG, prompt engineering)

Preferred

- Healthcare insurance / Managed Care (MCO) experience — familiarity with claims, clinical workflows, risk models, or regulatory frameworks is a strong plus.

- Experience with vector databases, hybrid semantic neural architectures, or agentic AI systems.

- Background in explainable or responsible AI.