Named Data Science Professional of the Year at the Waters Women in Technology & Data Awards, Lightkeeper’s Managing Director of Analytics has spent a decade helping investment teams turn historical portfolio data into decisions that actually move the needle.
Before Mary Viviano ever helped a client understand their trading patterns, she was the one trying to figure them out herself.
Early in her career, Mary was doing the kind of portfolio analysis she now builds sophisticated tools for, only she was doing it in Excel, manually, with whatever data she could get her hands on. Later, at another firm, she discovered Lightkeeper and realized how much more was possible when the data was properly structured. She became a user before she ever became an employee.
That experience shapes everything about how she works today.
“I’ve sat in their seat,” she says simply. “I know what they’re trying to solve for.”
The Problem with a Single Number
Mary joined Lightkeeper in 2016 and has spent the decade since helping clients unlock something most investment firms are sitting on without fully realizing it: the story inside their own portfolio history.
The starting point, she explains, is recognizing what a standard P&L number doesn’t tell you.
“I made 300 basis points in Microsoft, but that doesn’t tell you anything. What would you have earned if you’d just invested in the market for that time period? What if you’d doubled down here, gone in more quickly or more slowly, exited differently?”
A single outcome number, she argues, can’t help you improve. To do that, you need to understand how the decision was made; the entry, the adjustments along the way, the exit, and whether each of those steps actually added value.
That’s the foundation of Trade Decision Analytics, the framework Mary developed at Lightkeeper that has become one of the platform’s most significant analytical advances. By decomposing trading activity into distinct phases and measuring how each contributes to long-term portfolio P&L, investment teams can start to identify consistent patterns in their own behavior and use them to make more informed decisions.
“If people understand that they have a pretty consistent bias in how they open positions or close positions, or how they react when certain things happen in the market, they can lean in,” she says. “Maybe: I’m really convicted in this name; I should put the full position on all at once and save myself money. Or: when a stock moves against me by 25% and I double down, that’s usually a bad idea. Understanding that and incorporating it into the investment process, that’s how you maximize returns.”
The Work Behind the Insight
What doesn’t come through in an award citation is how hands-on Mary’s work actually is.
When a client needs analysis, she goes into their data manually, works through the statistics she thinks will tell the most useful story, and builds out a deck they can review together. It’s time-intensive, deliberate work, less data engineering, more portfolio coaching.
“The most rewarding part is helping clients see patterns in their own data that they hadn’t noticed before,” she says.
New analytics at Lightkeeper usually start with a client conversation, not a specific feature request, but a question a client is wrestling with. Mary and her colleagues dig into it, develop an approach, and then take it back to clients for feedback.
“We can’t just create new analytics in a vacuum. It has to be based on discussions with clients, making sure it’s going to be useful, that it’s understandable, that clients can access it without it taking 35 minutes to get an answer.”
Often, one question becomes five. “Someone might ask for something specific and when you dig in, it turns into several things, because you realize someone else was asking a similar question, just in a different way. It’s iterative.”
Building for the Long Term
Beyond the analytics themselves, Mary has worked to make Lightkeeper’s knowledge more accessible to clients directly. About two years ago, she and colleague Stephen Scherock developed the Knowledge Center, a searchable resource library where clients can find detailed explanations of analytics and methodologies without needing to pick up the phone or send an email.
It grew out of a practical problem: Mary and Stephen had built up a library of explanatory documents they’d send out whenever a client asked a question. Putting them somewhere clients could find on their own just made sense.
It’s also, she notes, the kind of well-structured content that will matter more as AI plays a larger role in how clients interact with data. “In the age of AI, for that content to be searchable and usable, not just a stat definition but a real explanation of how something works, I think that’s really valuable.”
On AI more broadly, Mary is measured. She sees real potential for it to make her own work more efficient, particularly the manual, server-by-server portfolio analysis she currently does by hand. But she’s clear-eyed about what won’t change.
“People still need to be able to understand it and translate the data to other people. If you’re in a position where you can do that, that’s a real benefit.”
Recognition She Didn’t See Coming
When asked what it felt like to win the Data Science Professional of the Year award at the Waters Women in Technology & Data Awards, Mary is characteristically understated.
“It’s not something I would ever even think about. The fact that colleagues took the time to think about me, to put my name in and do all the work for the nomination, that’s what I’m really appreciative of.”
She’s not someone who seeks the spotlight, she admits. But for those who work with her, the recognition landed exactly right.
“Mary has a unique ability to bridge the gap between sophisticated analytics and real-world investment decisions,” says Dean Schaffer, CEO of Lightkeeper. “She understands the technical complexity of the data, but just as importantly, she understands how investment teams actually work. That combination allows her to turn analytics into insights that genuinely help clients improve their investment process. One of the greatest value-adds that Lightkeeper can provide to our clients is access to and insights from Mary.”
Greg Johnson, Senior Managing Director, Client Solutions of Lightkeeper, puts it this way: “She doesn’t just build analytics, she works closely with clients to understand their challenges and helps them apply the insights in meaningful ways.”
She also has a message for young women considering a career in data science. “If you like math and turning data into actionable insights, I’d definitely suggest it,” she says. “It’s market adjacent, which means it’s constantly changing and exciting. And with AI making such a difference, if you’re in a position to understand it and translate it to other people, that’s a real benefit.”
For Mary, the work and the recognition point to the same thing: data is only as useful as what you do with it.
“The data is already there,” she says. “The real value is helping teams understand what it’s telling them.”
