7 Key LaTeX Formatting Principles for Data Science CVs in 2025 A Statistical Analysis

I recently spent a rather unproductive afternoon staring at a colleague's CV, admiring the crispness of the typography but simultaneously feeling a familiar pang of frustration. We spend countless hours wrestling with model convergence, debugging obscure library dependencies, and optimizing kernel performance, yet when it comes to presenting that work to a potential collaborator or employer, we often default to the most visually bland, easily parsed documents imaginable. This isn't just about aesthetics; in the data science hiring pipeline, especially for roles demanding rigorous quantitative skill, the presentation of your technical history speaks volumes about your attention to detail—or lack thereof. If your CV looks like it was slapped together in a basic word processor, I have to wonder how much care you put into tuning that final regularization parameter.

The common refrain is to use LaTeX, and for good reason; it handles mathematical notation and consistent spacing far better than any WYSIWYG editor I have encountered. However, simply using a standard template isn't enough anymore. As of late 2025, the expectations for a statistically-aware reader scanning technical applications have subtly shifted. They are looking for specific, almost subconscious cues about your command over structure and precision, cues that LaTeX, when used correctly, can deliver. I started to map out what I consider the non-negotiable formatting tenets that separate the truly professional technical document from the merely adequate submission.

Let’s zero in on the typography and environment control, because this is where most engineers stumble when moving beyond simple text blocks. I am referring specifically to the consistent application of font families for different data types within your document structure. When citing code snippets, library names, or specific variables, they must be rendered in a monospaced font, obviously, but the key is ensuring that the default document font—the one used for your narrative descriptions—is a high-quality serif font like Computer Modern Roman or Palatino, providing superior readability for dense textual descriptions of methodology. Furthermore, the spacing around inline math operators, like the equals sign or the plus symbol, needs meticulous handling; often, default LaTeX settings introduce too much horizontal room, making equations look disjointed from the surrounding text flow. I adjust the `\thickmuskip` and `\medmuskip` parameters slightly inward for cleaner integration when writing sentences like "The loss function $L(\theta) = \sum (y_i - \hat{y}_i)^2$ was minimized." Pay close attention to how section headings are rendered; overly large font sizes scream amateur, whereas a subtle shift in weight and a small caps treatment, controlled via the `sectsty` package, provides necessary visual hierarchy without shouting. Finally, the management of whitespace on the page is not arbitrary; ensuring margins are set to approximately 0.75 inches on all sides provides a clean border that prevents the text from feeling cramped, a small detail that nonetheless impacts the reader's visual processing speed immensely.

The second area demanding rigorous attention involves the structuring of your project descriptions and the consistent handling of citations, even if you are not submitting a formal paper. When detailing a specific data science project, avoid long, unbroken paragraphs describing the entire pipeline; instead, use a highly structured, albeit condensed, format where each major step—Data Acquisition, Preprocessing, Modeling, Validation—is clearly demarcated using small, bolded labels followed by a single, dense sentence describing the action taken. This allows a reviewer to rapidly assess your workflow proficiency without reading prose. Consistency in bibliography style is another subtle indicator of discipline; even if your CV only lists three external resources, using a standardized style like plain or unsrt via BibTeX ensures that the formatting of dates, authors, and titles remains uniform, signaling that you respect established documentation standards. For lists of achievements or skills, avoid the default LaTeX itemization environments if possible; I prefer custom environments that allow for tighter vertical spacing between list items, often reducing the default `\itemsep` by a factor of two to pack more information onto the page without inducing visual clutter. Moreover, the use of subtle horizontal rules, perhaps using the `\hrulefill` command judiciously between major job roles rather than relying solely on bold section breaks, adds a non-distracting structural element guiding the eye down the page. Observe how the vertical rhythm of the document is maintained; inconsistent spacing between different environments—say, between a figure caption placeholder and a subsequent paragraph—is immediately jarring to a trained technical eye. This precision, or lack thereof, subtly informs the reader about the level of discipline you apply to the rest of your technical work.