Top 12 Actuary Skills to Put on Your Resume

In the highly analytical and detail-centric world of actuarial science, the skills you spotlight on your resume can tilt the odds in your favor. The right stack signals fluency in risk, credibility in forecasting, and a habit of making messy data behave. Below, a tight set of 12 actuary skills that consistently matter to hiring managers, sharpened for today’s tools and expectations.

Actuary Skills

1. Python
2. R
3. SQL
4. Excel
5. SAS
6. Tableau
7. VBA
8. Power BI
9. Risk Management
10. Financial Modeling
11. Data Analysis
12. Machine Learning

1. Python

Python is a broad, batteries-included language actuaries use for data wrangling, statistical modeling, simulation, and automation. Fast to prototype, sturdy enough to scale.

Why It's Important

Because it unifies data prep, analytics, and reporting in one place. From risk models to pricing engines, Python accelerates iteration and keeps analyses reproducible.

How to Improve Python Skills

1. Nail the core stack: Pandas for tables, NumPy for arrays, SciPy and Statsmodels for inference and time series, scikit-learn for classic ML.
2. Visualize with intent: Matplotlib and Seaborn for crisp charts; plot only what answers the business question.
3. Automate the grunt work: Build Jupyter notebooks that parameterize reports, schedule them, and log results.
4. Engineer reliability: Write tests (pytest), lint code (ruff/flake8), and package reusable functions.
5. Practice on actuarial tasks: Loss triangles, GLMs, survival models, stochastic simulations. Small projects; real data when possible.
6. Collaborate: Read others’ code, review pull requests, and adopt version control habits.

Keep iterating. Ship small, improve quickly, document what matters.

How to Display Python Skills on Your Resume

2. R

R is purpose-built for statistics and elegant graphics. Actuaries lean on it for GLMs, credibility, reserving analysis, and reproducible reporting.

Why It's Important

It shines where modeling depth and clear visualization meet. The ecosystem is rich with packages for actuarial workflows and rigorous inference.

How to Improve R Skills

1. Adopt tidy workflows: dplyr, tidyr, purrr, and ggplot2 for readable pipelines and polished visuals.
2. Actuarial packages: Explore tools such as actuar and related libraries for loss distributions, credibility, and risk measures.
3. Reporting as code: R Markdown or Quarto for parameterized, auditable reports and dashboards.
4. Modeling breadth: GLMs, GAMs, survival analysis, mixed models; validate with resampling and diagnostics.
5. Community and practice: Contribute examples, replicate published analyses, and pressure-test your code style.

How to Display R Skills on Your Resume

3. SQL

SQL is the lingua franca for querying, joining, and shaping relational data. Actuarial datasets are large, tangled, and business-critical—SQL makes them tractable.

Why It's Important

Because every serious analysis starts with reliable data extraction and validation. Without sound queries, downstream models wobble.

How to Improve SQL Skills

1. Think in sets: Master joins, window functions, CTEs, and subqueries. Prefer set-based logic over row-by-row operations.
2. Optimize early: Read query plans, design indexes, and reduce data movement. Small changes, big speedups.
3. Data hygiene: Validate constraints, handle nulls deliberately, and normalize where appropriate.
4. Warehouse savvy: Understand differences across platforms (e.g., Snowflake, BigQuery, PostgreSQL, SQL Server).
5. Practice: Rebuild common actuarial pulls—exposures, claims development, premium earned by cohort—and benchmark performance.

How to Display SQL Skills on Your Resume

4. Excel

Excel remains the actuarial workhorse for analysis, reconciliation, and ad hoc modeling. Fast, familiar, and everywhere.

Why It's Important

Stakeholders live in spreadsheets. Clear models and tidy sheets speed decisions and audits.

How to Improve Excel Skills

• Modern functions: Prefer XLOOKUP over VLOOKUP, use FILTER/UNIQUE/SORT, and embrace dynamic arrays, LET, and LAMBDA to reduce brittleness.
• Pivot mastery: Build clean pivot tables and charts; refresh from structured tables for stability.
• Scenario logic: Data Tables, Solver, and sensitivity layouts for pricing and capital questions.
• Model hygiene: Separate inputs, calculations, and outputs. Name ranges sparingly; avoid volatile formulas where possible.
• Automation: Where repetition creeps in, script it (VBA or Office Scripts) and log assumptions.

How to Display Excel Skills on Your Resume

5. SAS

SAS is a mature analytics suite widely used in regulated environments. Reliable data steps, robust procedures, and strong governance.

Why It's Important

Many insurers rely on SAS for production reporting, pricing analytics, and risk modeling. Its auditability and stability are valued by control functions.

How to Improve SAS Skills

1. Core fluency: DATA step, PROC SQL, PROC SUMMARY/MEANS, and statistical procedures commonly used in pricing and reserving.
2. Macro language: Parameterize workflows, reduce duplication, and standardize reporting packs.
3. Performance: Indexing, formats, and memory-savvy data steps for large tables.
4. Documentation-first: Clear logs, comments, and output reconciliation to ease audits.
5. Hands-on access: Use SAS OnDemand for Academics (free) to practice when you don’t have enterprise access.

How to Display SAS Skills on Your Resume

6. Tableau

Tableau turns sprawling datasets into interactive visuals. Actuaries deploy it to surface trends, communicate uncertainty, and let stakeholders explore without breaking anything.

Why It's Important

Visual stories move decisions faster than dense tables. Good dashboards trim meetings and sharpen questions.

How to Improve Tableau Skills

1. Design that breathes: Thoughtful layouts, restrained color, and intentional focus. Minimize ink that doesn’t inform.
2. Calculated fields: Build metrics for loss ratios, frequency/severity, credibility blends, and development factors.
3. Data modeling: Blend or relate sources properly; pre-aggregate where it helps responsiveness.
4. Interactivity: Parameters, highlight actions, and drill paths that match how actuaries analyze portfolios.
5. Governance: Use Tableau Server/Cloud for permissions, versions, and certified data sources.

How to Display Tableau Skills on Your Resume

7. VBA

VBA (Visual Basic for Applications) stitches automation into Excel. Useful when recurring tasks bog down your day.

Why It's Important

It slashes manual effort, reduces copy-paste errors, and standardizes calculators and reports used across teams.

How to Improve VBA Skills

1. Clean structure: Modularize subs and functions, pass arguments explicitly, and avoid global variables.
2. Solid error handling: Defensive coding, graceful exits, and logging that helps non-technical users recover.
3. Speed tricks: Turn off screen updating, work with arrays, and write back in bulk.
4. User experience: Simple forms, clear prompts, and obvious outputs.
5. Know when to switch: For web-based workflows, consider Office Scripts or Python add-ins if they fit your environment.

How to Display VBA Skills on Your Resume

8. Power BI

Power BI brings together modeling, DAX calculations, and interactive reporting inside the Microsoft ecosystem.

Why It's Important

It connects cleanly to enterprise data, scales from personal dashboards to governed apps, and supports row-level security for sensitive metrics.

How to Improve Power BI Skills

1. DAX depth: Time intelligence, filter context, and iterator functions for earned premium, development, and retention metrics.
2. Power Query: Deterministic, auditable transforms that tame messy source data.
3. Data modeling: Star schemas, relationship direction, and summarized tables for performance.
4. RLS and deployment: Secure access by role, then move from dev to test to prod with discipline.
5. Fit-for-purpose visuals: Custom visuals only when native charts can’t carry the story.

How to Display Power BI Skills on Your Resume

9. Risk Management

Risk management, for actuaries, means spotting financial hazards early, measuring them plainly, and designing responses that keep firms solvent and steady.

Why It's Important

It protects balance sheets, supports pricing integrity, and aligns with regulatory expectations—from ORSA to capital adequacy and stress testing.

How to Improve Risk Management Skills

1. Education that sticks: Keep current with frameworks, capital models, and emerging risks (cyber, climate, longevity).
2. Quant rigor: Model tails thoughtfully—scenario analysis, EVT, copulas where warranted—then validate assumptions.
3. Controls and governance: Document methodologies, maintain model inventories, and track limitations transparently.
4. Regulatory awareness: Monitor updates in reserving, solvency, accounting, and reporting requirements.
5. Communication: Translate technical risk into actionable decisions for boards and non-technical leaders.

How to Display Risk Management Skills on Your Resume

10. Financial Modeling

Financial modeling converts assumptions into numbers you can probe—pricing, ALM, capital, profitability, and more.

Why It's Important

Good models clarify trade-offs. They reveal sensitivity, inform strategy, and withstand audits.

How to Improve Financial Modeling Skills

1. Structure first: Separate inputs, logic, and outputs. Build for reviewability and change.
2. Method breadth: From GLMs and credibility to stochastic simulation, interest rate models, and cash flow projections.
3. Validation discipline: Back-testing, challenger models, and independent reviews to curb hidden bias.
4. Documentation and lineage: Assumptions, sources, parameter history, and versioning—all traceable.
5. Business fluency: Understand products, reinsurance, investment levers, and accounting impacts so your model mirrors reality.

How to Display Financial Modeling Skills on Your Resume

11. Data Analysis

Data analysis is the bedrock: clean, explore, test, conclude. Then tell the story without blurring the caveats.

Why It's Important

Better questions, better features, better decisions. Sound analysis shrinks uncertainty and exposes the levers that matter.

How to Improve Data Analysis Skills

1. Stat strength: Probability, inference, diagnostics, and careful treatment of missingness and outliers.
2. Reproducibility: Version control, parameterized reports, and scripted data pipelines.
3. Domain context: Claims processes, underwriting rules, seasonality, and regulatory constraints guide the analysis you choose.
4. Visualization with restraint: Pick charts that reveal, not distract. Label clearly. Include uncertainty where feasible.
5. Peer review: Seek critiques, replicate results, and pressure-test assumptions before publishing.

How to Display Data Analysis Skills on Your Resume

12. Machine Learning

Machine learning adds predictive punch—classification, regression, anomaly detection—when patterns are complex and volumes are high.

Why It's Important

Used well, ML improves pricing precision, fraud detection, lapse prediction, and claims triage. Used carelessly, it confuses stakeholders and invites model risk.

How to Improve Machine Learning Skills

1. Data first: Robust feature engineering, leakage checks, and careful splits. Garbage in still means garbage everywhere.
2. Right tool, right job: Strong baselines (GLMs, regularized models), then tree ensembles (XGBoost/LightGBM), and only then heavier methods if justified.
3. Tuning and validation: Cross-validation, calibration, and proper error metrics aligned to business cost.
4. Interpretability: Global and local explanations, monotonic constraints where appropriate, and clear documentation for governance.
5. Monitoring: Track drift, recalibrate periodically, and keep a rollback plan. Models age; plan for it.
6. Ethics and fairness: Test for bias, define guardrails, and record decisions that affect customers.

How to Display Machine Learning Skills on Your Resume