Market Intelligence Analyst, Advisory Services

OpenHouse; Calgary; On-site; CAD 95,000 - 135,000; Full time

Job summary

A leading AI research firm in Calgary is seeking a founding analyst for their Client Intelligence team. This role involves designing analyses, creating decision-ready reports, and working closely with clients to turn insights into actionable strategies. Ideal candidates have strong backgrounds in quantitative fields and experience with SQL and Python. This full-time position offers competitive compensation and growth opportunities within the company.

Benefits

Vacation & Paid Time Off

Dental and Medical Benefits

Qualifications

• Experience must include working with SQL and Python for analysis.
• Strong communication skills to create concise executive memos.
• Background in quantitative disciplines such as Data Science or Applied Economics.

Responsibilities

• Design and execute studies to provide decision-ready insights.
• Work closely with the Engagement Manager to shape client narratives.
• Maintain logs of assumptions and decisions for auditability.

Skills

Analytical mindset

Structured problem solving

Exceptional communication

Technical fluency in SQL and Python

Strong quantitative foundation

Education

Applied Economics

Statistics

Data Science

Tools

SQL

Python (Pandas / NumPy)

Excel

Looker / Tableau / Power BI

Job description

About You

You thrive on structuring ambiguity with proven consulting frameworks (SCR / MECE) and turning analysis into action—and you communicate with one-message exhibits and pyramid-structured writing that drive decisions. You're ready to go deep in a single industry and see the impact of your work over time. We welcome candidates from rigorous, quantitatively oriented disciplines—such as Marketing Analytics, Applied Economics, Statistics, Operations Research, Data Science, Business Analytics, or Finance (quant focus).

• These backgrounds illustrate the toolkit we value most:
• structured thinking and quantitative problem-solving, translated clearly into action
• at the intersection of data, strategy, and client engagement

About Us

is the leading AI research and deployment company transforming the $300B+ new home construction industry. Our vision is to be the

operational intelligence backbone of home building

unifying data, enabling decisive action, and powering a connected ecosystem where every participant can harness the benefits of AI to shape their own future. We help technology-minded home builders make smarter, data-driven decisions by delivering predictive insights, streamlining operations, and enabling strategies that drive profitable growth and resilience. Our Quantitative AI platform is

deployed across 30+ geo-markets in the U.S. and Canada.

About the Role

As the founding analyst on our Client Intelligence team, reporting directly to the CEO, you'll be the power user of our AI engine—designing and executing studies and turning outputs into decision-ready exhibit packs. Paired with an Engagement Manager who leads the client storyline, you'll co-create the narrative by supplying the analysis, exhibits, and options / trade-offs. You'll work in SQL and Python notebooks (analysis, not engineering), keep everything reproducible, documented, and auditable, and join select client sessions to capture firsthand context and data.

What You'll Do

• Structure & scope: Use SCR / MECE to frame the learning agenda, hypotheses, and data questions with the Engagement Manager.
• Build the storyline inputs: Convert analysis into one-message exhibits and concise executive memos (observations, interpretations, trade-offs, recommendations, next steps).
• Code to analyze (not to engineer): SQL for joins / cleans; Python notebooks (Pandas / NumPy) for Exploratory Data Analysis, forecasting, and pricing work; ensure end-to-end reproducibility.
• Grounded in the field: Participate in client interviews or on-site meetings to capture context, translate observations into data requirements, and model assumptions.
• Decision artifacts: Maintain assumptions logs, decision logs, and sensitivity tables so recommendations are auditable.
• Leverage the platform: deep-dive on outputs from our Quantitative AI engine; build frameworks, scenario models, and decision-ready visuals.
• Methods in practice: Apply forecasting, elasticity / WTP, cohort / CLV, funnel diagnostics, and causal / DiD where appropriate—and document why.

Methods We Use

• Method literacy: select, interpret, and defend the right approach for the decision at hand.
• Marketing & Growth Analytics: funnel diagnostics, cohort / retention & Customer Lifetime Value, attribution-aware performance readouts, segmentation / clustering.
• Revenue & Forecasting: time-series forecasting, seasonality / trend decomposition, demand shaping, mix & channel optimization.
• Pricing & Monetization: price elasticity, cross-elasticities, Willingness to Pay, promo / discount impact, scenario modelling.
• Causal Inference & Experimentation: A / B tests and quasi-experiments (Difference-in-Differences, synthetic controls, matching), uplift modelling, guardrails.
• Risk & Sensitivity: Monte Carlo, sensitivity analysis, back-testing, error analysis and confidence intervals.

How we work

• Notebooks & versioning: clear cell ordering, narrative markdown, and Git-based version control.
• Data provenance: source IDs, extraction dates, joins / filters documented; tidy datasets with data dictionaries.
• Auditability: parameters and assumptions logged; results are re-runnable end-to-end.
• Exhibit discipline: charts / tables labelled with definitions, time windows, and caveats; one-slide summaries per analysis block.
• No mystery numbers: every exhibit traces back to a versioned notebook and a tidy dataset.

What You'll Bring

• Analytical mindset & structured problem solving: SCR / MECE with a disciplined approach to data quality, visualization, and reproducibility.
• Exceptional communication: pyramid-structured writing, one-message exhibits, and plain-English insights that move executives to action.
• Technical fluency for analysis: SQL + Python notebooks (Pandas / NumPy) to produce clean, auditable analysis (versioning, tidy datasets, documented assumptions).
• Range across tools: from Excel to databases, and from Looker / Tableau / Power BI to Python scripts for deeper insight.
• Strong quantitative foundation, e.g., Applied Economics, Statistics, Marketing Science, Operations Research, Data Science, Business Analytics, Finance / CFA.

Bonus Points

• Experience creating exec-ready mini-decks (3–7 pages) and decision memos from analytical work.
• Exposure to pricing / monetization, forecasting, or experimentation in a client context.
• Internship or early experience in consulting / tech advisory with light SQL / Python.
• Familiarity with housing, construction, or real estate economics.

What Success Looks Like (Your First 90 Days)

First Week

• Set up tools (Google Cloud Platform Access, Python notebooks, Git) and learn our AI platform.
• Shadow the Engagement Manager in a client session.

First 2 Weeks

• Complete a small analysis task (e.g., trend or funnel diagnostic) and contribute your first exhibit.
• Document your workflow to build reproducibility habits.

First 30 Days

• Own a defined analysis stream with guidance from the Engagement Manager.
• Join client sessions to capture context and translate it into structured data questions.

First 60 Days

• Deliver your first end-to-end study (e.g., pricing, elasticity, or forecasting) with clearly documented assumptions and sensitivities.
• See your first independent analysis included in an executive readout by the Engagement Manager.

First 90 Days

• Ship reproducible notebooks for at least one engagement-common analysis.
• Be the go-to analyst on an active client project, producing decision-ready analysis.
• Have your work recognized by clients as decision-enabling, not just interesting.

Growth

High performers typically progress to Senior Analyst, then Engagement Manager within 12–24 months based on impact and readiness.

Benefits

• Vacation & Paid Time Off
• Dental and Medical Benefits

Compensation: $95,000.00-$135,000.00 per year