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About This Role
About Megaport
We’re not your typical tech company – and we don’t want to be. Megaport is the global leader in Network as a Service (NaaS), and has transformed the way businesses connect to the cloud, data centers, and each other. We’re publicly listed on the Australian Stock Exchange and partnered with the biggest names in tech like Amazon, Microsoft, Google, Oracle, IBM, and more. Headquartered in Brisbane with a crew of over 600 people spread across Asia\-Pacific, Europe, and the Americas, our employees enjoy an environment that is collaborative, supportive, and (actually) fun.
Our Team Culture
We’re a team of problem solvers, pixel pushers, code slingers, and cloud fanatics. Culture is more than a poster on the wall here – collaboration beats hierarchy, curiosity fuels our growth, and everyone’s voice matters. We take our work seriously, but not ourselves. We work across time zones to execute on our global vision, trust each other to get things done, and never compromise our values for commercial gain. Most importantly, we place our customers at the center of everything we do.
We’re committed to increasing representation in the tech industry and welcome applicants from all backgrounds. Don’t meet every requirement? That’s okay. If you’re excited about this role, we encourage you to apply.### The Role
Megaport is seeking an experienced and business\-oriented Data Scientist to help scale advanced analytics and operational intelligence capabilities across the Revenue organization. This Data Scientist will partner closely with Revenue Operations, Data Engineering, Finance, Systems, and Commercial leadership teams to improve forecasting, operational visibility, AI readiness, and data\-driven decision making. This role will help build advanced analytics capabilities supporting Sales Operations, Revenue Operations, forecasting, customer insights, operational efficiency, and AI initiatives. The ideal candidate combines strong technical and analytical expertise with the ability to translate complex data into actionable business insights.
### What You’ll Be Doing
- Develop predictive forecasting models and pipeline analytics frameworks
- Analyze sales productivity, customer behavior, operational efficiency, and revenue trends
- Build scalable dashboards, models, and operational intelligence solutions
- Partner with Revenue Operations and Finance teams to improve KPI governance and reporting consistency
- Support AI readiness initiatives through data quality analysis and modeling
- Collaborate with Data Engineering teams to improve GTM data architecture and integration quality
- Identify operational inefficiencies and recommend data\-driven process improvements
- Develop analytical frameworks supporting marketplace, channel, and consumption\-based business models
- Support executive decision making through advanced reporting and business analysis
- Assist with data governance, metric standardization, and forecasting transformation initiatives
- Build models supporting customer expansion, renewals, and churn analysis
### What We Are Looking For
- 5\+ years of Data Science, Analytics, or Revenue Intelligence experience
- Strong SQL, Python, and statistical modeling expertise
- Experience working with large operational and sales datasets
- Experience building forecasting and predictive analytics models
- Strong data visualization and dashboarding capabilities
- Experience partnering with business stakeholders and executive teams
- Strong understanding of SaaS or technology business models
- Ability to translate technical findings into business recommendations
### What We Offer
- Flexible working environments
- Birthday Leave, 12 weeks of parental leave (after 12 months continuous service), and 5 days of study leave.
- Creative, fun, and contemporary work environments
- Motivated team of industry experts and plenty of learning opportunities
- Celebrated success with internal awards programs
- Health and wellness program
- Generous performance bonus structure
###### \#DNI
If you have any questions, please reach out to Megaport's Talent Acquisition Team at [email protected]
NOTE: *All Megaport business correspondence is conducted via our business email accounts (@megaport.com). If you have any concerns, please reach out to Megaport's careers team [email protected] directly and we will verify the legitimacy of any communication. Megaport will not ask you to create an account via Microsoft teams, and does not associate with any email accounts under "@megaportau.com".*
*All applications will be treated in confidence.*
*Please see Part 2 of our Privacy Policy to see what information Megaport collects from job applicants, why, and how we store and use it. Note that you’re entitled to know what personal data of yours Megaport holds, to request updates, rectification, and in some circumstances restriction or deletion thereof if you object (you being entitled to withdraw your consent to our holding your information at any time). Please see Part 5 of our Privacy Policy for more details on this and how to contact Megaport's data protection officer if you have any further privacy\-related questions. Candidates who meet the selection criteria will be invited to attend an interview. Strictly no Recruitment Agencies.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Megaport, 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 $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Megaport AI Hiring
Megaport has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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