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About This Role
Title: System Integration Engineer
Location: Albany, NY
50% Onsite Work
Length: 36 Months\+
A System Integration Engineer defines and implements strategies and technology that ensures the seamless integration of data and application interfaces between
business stakeholders and IT teams by eliciting, documenting, and analyzing requirements to design, configure, and deploy Salesforce solutions. They must always
translate business needs into functional specs, manage system enhancements, and ensure optimal performance.
Key Responsibilities
- Requirements Gathering \& Analysis: Conduct stakeholder workshops to elicit, analyze, and document business requirements, transforming them into user stories and technical specifications.
- System Configuration \& Development: Configure Salesforce functionality (workflows, validation rules, security models, flows) and support developers in creating Apex, Visualforce, and Lightning components.
- Process Mapping \& Optimization: Analyze current state processes to identify inefficiencies, designing future state solutions using process mapping tools.
- Testing \& Deployment: Lead User Acceptance Testing (UAT), create test scripts, and coordinate deployment across Salesforce sandboxes.
- Data Management \& Reporting: Perform complex data analysis, build reports and dashboards, and maintain data integrity.
- Support \& Documentation: Troubleshoot support issues, create documentation (user manuals, release notes), and provide training.
Required Technical Skills
- Salesforce Platform: Proficient in Sales/Service Cloud, Lightning Experience, and declarative automation tools (Flows).
- Technical Knowledge: Understanding of Salesforce object modeling, APIs, and data integration patterns.
- Methodologies: Experience with Agile/Scrum and project management lifecycles.
- Tools: Proficient in process mapping software (Visio/Lucidchart), SQL, and data loader tools.
Required Soft Skills
- Communication: Ability to bridge communication between technical teams and non\-technical business users.
- Problem\-Solving: Strong analytical capabilities to resolve complex business challenges.
- Project Management: Ability to manage multiple, complex projects simultaneously.
Mandatory Qualifications
Level II\- More than seven (7\) years of experience working on complex projects with 2 or more years in a leadership role as a System Integration Engineer
More than two (2\) years of Salesforce architecture and solution design experience.
Minimum two (2\) years experience in using Salesforce Service Cloud and CRM.
Desirable Qualifications
Bachelor's degree in computer science, Information Systems, or a related field.
Salesforce Certified Business Analyst and Administrator certifications.
Two (2\) Years Experience with CPQ, Pardot, or specialized Salesforce Industries clouds.
Two (2\) Years experience in Sales/Service Cloud, Lightning Experience, and declarative automation tools (Flows).
Two (2\) Years experience Salesforce object modeling, APIs, and data integration patterns.
Two (2\) Years experience in process mapping software (Visio/Lucidchart), SQL, and data loader tools.
Two (2\) Years experience in Requirements Gathering \& Analysis: Conduct stakeholder workshops to elicit, analyze, and document business requirements, transforming
them into user stories and technical specifications.
Two (2\) Years experience in System Configuration \& Development: Configure Salesforce functionality (workflows, validation rules, security models, flows) and support
developers in creating Apex, Visualforce, and Lightning components.
Two (2\) Years experience in Process Mapping \& Optimization: Analyze current state processes to identify inefficiencies, designing future state solutions using process
mapping tools.
Two (2\) Years experience in Testing \& Deployment: Lead User Acceptance Testing (UAT), create test scripts, and coordinate deployment across Salesforce sandboxes.
Job Type: Contract
Pay: From $38\.05 per hour
Expected hours: 40 per week
Experience:
- Data science: 7 years (Required)
- exploitation: 7 years (Required)
Security clearance:
- Top Secret (Required)
Work Location: Hybrid remote in Albany, NY 12244
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 26,159 AI roles we're tracking, Data Scientist positions make up 2% of the market. At Vcarve, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $204,700 based on 441 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Vcarve AI Hiring
Vcarve has 1 open AI role right now. They're hiring across Data Scientist. Based in Albany, NY, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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