Interested in this Data Scientist role at AZAD Technology Partners?
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About This Role
Join AZAD Technology Partners as a Data Scientist and work within the facility's Transmission Infrastructure Asset Management organization on the Strategy and Planning team. This assignment will provide support for projects under the program by providing analytical expertise for planning, development, integration, and implementation of asset management technologies, methods, and standards for interim and long\-term solutions to manage risk and spend efficiency on the facility's transmission system. This assignment will analyze, process and model data and interpret the results to develop data\-driven solutions. This is a 2 year plus consultant position with high probability of long term and ongoing continued employment. This position is hybrid based in Vancouver, WA with onsite 3 days per week (Tuesday, Wednesday and Thursday). The hourly pay rate for this position is between $65\.00 and $70\.00 depending on related qualifications and experience, as well as composition of selected compensation package. The ideal candidate must possess the minimum qualifications:
A Bachelor's or Master's degree in advanced mathematics, computer science, machine learning, or statistical methods is required:
With a Master's degree, 7 years' of hands\-on experience performing the following is required
With a Bachelor's degree, 9 years' of experience is required:
Manipulating data sets, querying databases, and building statistical models
Statistical or data mining techniques
Using Web Services
Analyzing data from 3rd party users
Developing data models and algorithms
Creating and using advanced machine learning algorithms and statistics
Knowledge and understanding of financial analysis/budgeting, risk analysis, probability and statistics, and electric utility operations
With a Bachelor's degree, at least 10 graduate credits in computer science algorithms, statistics, software design, or data management OR one of the following Data Science Certifications or similar are also required:
Certified Analytics Professional (CAP)
Data Science Council of America (DASCA) Senior Data Scientist (SDS)
Data Science Council of America (DASCA) Principle Data Scientist (PDS)
Dell EMC Data Science Track
Google Certified Professional Data Engineer
Google Advanced Data Analytics Certificate for Machine Learning
IBM Data Science Professional Certificate
Mathematics experience including multivariate calculus, linear algebra, differential equations, and real analysis:
Probability and Statistics: including stochastic processes, classical inference techniques, maximum likelihood estimation, Bayesian methods, Monte Carlo, and bootstrapping.
Computer Science: design and analysis of algorithms and data structures, computational complexity, search methods.
Supervised Learning (e.g., regression techniques, regularization techniques, ridge regression, ensemble methods, optimization through linear programming and convex optimization, nonlinear programming).
Unsupervised Learning (e.g., clustering techniques, hierarchical clustering, dimensionality reduction, principal component analysis).
Time Series Analysis.
Demonstrated knowledge of computer languages including, but not limited to Python, Java, SQL, and R. Demonstrated knowledge of distributed or parallel processing techniques used in the analysis and processing of large data sets.
Skill in discerning the strengths and weaknesses of various best\-practice quantitative solutions for a given real\-world problem and skill in developing new quantitative approaches to cater to particular features as needed when standard assumptions are inappropriate.
Using considerable judgment, proven ability to take vague or broadly defined goals or business objectives and translate them to questions that can be answered or problems that can be addressed via data driven analysis.
Demonstrated ability to communicate and present proposals, findings, and recommendations, both written and orally, to senior staff, management and executives and to external parties (e.g., representing BPA regionally such as to key stakeholders, customers, industry organizations, or regulators).
Valid U.S. Driver's License or RealID is required. Preferred Skills:
Knowledge of GIS and Asset Management Systems.
Experience with R software product(s).
Energy/utility industry experience.
Experience with Power BI (Microsoft Business Intelligence).
AZAD is looking for bright, talented, flexible, and customer centric problem solvers who enjoy the challenges associated with solving the most complex problems by utilizing the most sophisticated technologies and strong people skills.
Founded and managed by technologists and engineers, AZAD is a leading provider of Consulting Services to Fortune 500 and innovative firms since 1992\.
AZAD's model provides a unique employment experience that is career path focused, relationship based and dedicated to advocacy and retention with an average tenure of 9 years.
Our collaborative approach to support our clients, projects and supporting our employees' career paths are the keys to our success. Join AZAD's professional team and enhance your career by being engaged with some of the most interesting projects in the Pacific Northwest.
If you enjoy working in such an environment, we encourage you to apply.
EXCELLENT EMPLOYEE BENEFITS including Co\-paid Medical, Dental and Vision Insurance, Cafeteria Plan, Paid Sick Leave, 401K Plan, Credit Union Membership, and Referral Bonus.
U.S. Citizens only for Federal Clearance Requirement.
AZAD values diversity \- in backgrounds and in experiences.
Since our inception, we have witnessed how our diverse workforce has thrived while contributing to the increases in innovation and advancements in the client organizations we serve.
AZAD is committed to Diversity, Equity \& Inclusion and is striving to build an even more diverse, inclusive team that reflects the people and communities where we live and work.
AZAD, Inc. is an equal opportunity employer that considers and employs qualified individuals based upon job related qualifications regardless of race, color, sex, religion, creed, physical or mental disability, veteran's status, sexual orientation, national origin, age or any other status protected under applicable local, state or federal law. AZAD takes affirmative action to employ and advance in employment qualified employees and applicants who are disabled, disabled veterans, recently separated veterans, Armed Forces services medal veterans, and other protected veterans.
AZAD, Inc. is a Certified Minority Owned Business and OFCCP compliant.
Salary Context
This $135K-$145K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At AZAD Technology Partners, 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 $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($140K) sits 29% below the category median. Disclosed range: $135K to $145K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AZAD Technology Partners AI Hiring
AZAD Technology Partners has 1 open AI role right now. They're hiring across Data Scientist. Based in Vancouver, WA, US. Compensation range: $145K - $145K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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