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Daniela Böhm, Data Scientistin bei solvistas

Daniela Böhm von solvistas erzählt in ihrem Interview, wie sie über Umwege Data Science entdeckt hat, was ihr an der Arbeit im Unternehmen gefällt und gibt Tipps für Neueinsteiger.

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Video Summary

In “Daniela Böhm, Data Scientistin bei solvistas,” Speaker Daniela Böhm outlines her path from a general upper‑secondary background through mathematics (statistics and business mathematics) to a Data Science master’s and into her role at solvistas. She highlights end‑to‑end work with customer data—from preparation and storage to analysis, statistics, machine learning, and AI—plus regular client interactions, varied project work, and assignments aligned with personal interests. Her advice: be open and curious, enjoy working with data and computers, and keep asking questions—the team is supportive and you learn over time.

From Math Lecture Halls to End‑to‑End Data Work: Daniela Böhm (solvistas GmbH) on Curiosity, Client Conversations, and the Full Data Cycle

A DevJobs.at recap of “Title: Daniela Böhm, Data Scientistin bei solvistas”

In this devstory session, we listened to Speaker: Daniela Böhm from Company: solvistas GmbH share a grounded account of how she found her way into data science and what her job looks like today. The first thing that struck us: her path wasn’t preordained. She describes finishing a “normale IS‑Oberstufe” without a fixed plan to land in tech. University didn’t start as Plan A for a technical career either. Instead, she began studying mathematics in Vienna, focusing on statistics and business mathematics, and only then discovered that “programming and Data Science are cool.” She followed that interest into a Data Science master’s and, after graduating, joined solvistas.

What follows in her story is a pragmatic picture of the role: a Data Scientist who handles “the whole process of what you can do with data.” That means receiving data from clients, preparing, storing, and “sorting” it, then moving into analysis and statistics, applying machine learning, and—importantly—recognizing that, as Daniela put it, “now AI is always part of it.” Because projects at solvistas involve clients, she also emphasizes regular conversations and contact as an integral part of the job.

Our notes below focus on what we learned listening to Daniela: how a career can grow from interest rather than certainty, why curiosity and asking questions accelerate learning, and what it means to work end‑to‑end with data in a setting where projects vary in length, scope, and team composition.

A non‑linear path: from statistics to Data Science

Daniela lays out a calm, relatable journey:

no hard commitment to tech right after school,
mathematics in Vienna (statistics and business mathematics),
the realization that programming and data science are genuinely fun,
a master’s in Data Science,
applying to solvistas and starting as a Data Scientist.

One line encapsulates the pivot:

“I discovered that programming and Data Science are cool, so I actually studied Data Science in my master’s.”

The important message for many early‑career engineers: you don’t need to know from day one that it’s going to be data science. Let interest lead the way, and double down when it clicks. Daniela’s path validates curiosity as a compass.

What Data Science means in her role: the whole process

Asked what she does as a Data Scientist, Daniela highlights one essential theme: she covers the entire process of working with data. In her words, that includes:

getting data from clients,
preparing, storing, and “sorting” it,
running analyses and statistics,
applying machine learning,
factoring in AI, which she notes is “always” part of it now,
and maintaining client conversations and contact along the way.

This is an end‑to‑end mindset. It’s not just about modeling; it’s about understanding data quality, structure, and persistence; translating business questions; grounding analyses statistically; and communicating findings so they land. The mix of technical depth and communication skills is clear in how Daniela describes her day‑to‑day: the back‑end‑flavored discipline of structuring and storing data paired with the interpretive work of analysis and the interpersonal layer of client conversations.

“Because we work with clients, part of my job is always client conversations and keeping in contact with them.”

For data roles, this blend—technical and communicative—is a lever for better outcomes. Good results follow from bringing stakeholders into the process, clarifying assumptions early, and contextualizing outputs.

The anatomy of data work: from ingestion to interpretation

Daniela’s outline invites a pragmatic look at each stage of the process—no buzzwords required, just applied craft.

1) Receiving data: start by clarifying expectations

“When we work with clients, we get data from them …” Embedded in that simple setup are the real challenges: data rarely arrive pristine. The earliest conversations can shape everything downstream. Key questions at this stage include:

What’s the source and intended decision context for the data?
Are coverage and consistency sufficient for valid inferences?
What gaps need to be addressed with the client before moving on?

Daniela’s emphasis on client contact suggests a rule of thumb: good results begin with shared understanding, not just at delivery time.

2) Preparing, storing, “sorting”: strengthening the foundation

Preparation often stays invisible but supports everything else. Daniela’s short phrase—“prepare, store, and sort the data”—captures a critical work package: structure the inputs, reconcile formats, handle missingness, identify outliers, and ensure persistence. Investing here pays off in reproducibility and interpretability later.

3) Analysis and statistics: making patterns visible

“Once you have that, you can do analyses and statistics …” This is where a mathematical base shines. Framing hypotheses, understanding variables, and qualifying uncertainty are table stakes for meaningful conclusions. Statistics isn’t an afterthought; it’s the substrate on which modern analytics rests.

4) Machine learning and AI: part of the picture now

“Machine learning. Now AI is always part of it—that’s a good point.” Daniela’s aside captures a common expectation in data projects today. Whether it’s classification, forecasting, or clustering, ML and AI questions surface quickly. The job is to match methods to problems realistically and be as transparent about limits as about capabilities.

5) Client conversations: translating needs and results

For Daniela, client calls are simply part of the job. That constant loop—clarifying needs, structuring requests, validating assumptions, presenting and refining results—embeds analysis in real‑world use. In other words, end‑to‑end isn’t just “raw data to model,” it’s also “first discussion to decision support.”

Project work at solvistas: variety, flexibility, teamwork

Daniela points to variety and project dependency as defining aspects of working at solvistas:

timelines and tasks vary: “There are short projects and long projects,”
you’re involved in “every step,” not just a narrow slice,
colleagues change from project to project,
you can “say what you enjoy,” which influences assignment to one project or another,
and outcomes emerge together: “as a team, something good usually comes out of it.”

This is the profile of an environment where adaptability and self‑organization matter. If you prefer a tightly bounded specialist role, this setup may require adjustment. If you’re energized by variety, ownership, and learning in context, it provides rich exposure.

Entry requirements? Less about checklists, more about mindset

Daniela’s take on prerequisites is refreshingly straightforward:

“You don’t need extreme prerequisites—you just need to be open and curious.”

She adds a few realities to be aware of:

interest in data: the intrinsic drive to see what’s inside the numbers,
working with computers: concentrated, tool‑heavy sessions are part of the job,
client contact: not everything happens behind a screen—conversations are a built‑in source of variety.

The focus shifts from fixed skills to a posture: curiosity and openness beat perfectionism, especially early on.

The habit that compounds learning: asking questions

Daniela underscores a simple practice that accelerates growth:

“What I find important is to always ask questions, because at the beginning I thought you had to know it all yourself.”

Her account describes a loop where questions trigger momentum, provided you bring openness to the table:

“You learn it over time; you get into everything,”
“if you’re open and ask questions,”
“colleagues are very nice and answer them,”
“then you just learn a lot and can get into everything.”

It’s a sketch of a team culture where curiosity is welcome. Competence grows not through solitary heroics but through systematic inquiry and shared iteration.

What developers and data newcomers can take away

From Daniela’s story and her framing of daily work, we distilled several practical insights:

1) Career paths don’t have to be linear—let interest lead.

Starting without an early technical commitment isn’t a disadvantage. If mathematics, data, and problem‑solving energize you, it’s natural to move toward Data Science later on.

2) End‑to‑end understanding beats tool silos.

“The whole process” is more than modeling. Understanding data, preparing and storing it, analyzing it, and communicating results all factor into impact.

3) Client contact is a productivity multiplier.

Early and honest conversations—clarifying assumptions and contextualizing outputs—prevent mismatches and align results to needs.

4) Questions are the fastest learning tool.

Especially in the beginning: don’t pretend to know—commit to knowing by asking. Treat your team as a learning environment.

5) Curiosity and openness are true prerequisites.

Technical depth grows with practice. The mindset to probe, clarify, and keep learning is harder to substitute and more valuable.

Putting the lessons to work: routines that fit Daniela’s playbook

If you want to anchor Daniela’s insights in your everyday practice, try these concrete routines:

Project kickoff questions
What decision will these data inform?
How will we recognize success—and what constraints do we accept?
What’s missing, and who can clarify it?
Make data preparation explicit
Document transformations and the rationale behind them.
Ensure persistence so analyses are reproducible.
Flag provisional assumptions for client confirmation.
Use statistical discipline
State hypotheses before modeling.
Communicate uncertainty rather than smoothing it away.
Translate results into the client’s language.
Position AI realistically
“AI is always part of it now”—expectations run high. Match methods to problems and be clear about data prerequisites and limits.
Institutionalize questions
Aim for at least one focused question to a colleague per day.
Track open questions for client calls and revisit them.
Run mini‑retros after phases: What did we ask? What should we have asked earlier?

These habits reflect the posture Daniela describes: stay curious, work methodically, communicate openly.

Short and long projects: learning in different rhythms

Daniela notes that solvistas runs both “short” and “long” projects and that tasks can change even midstream. For individual growth, that mix fosters complementary strengths:

Short projects develop quick problem framing, focused delivery, and succinct communication—ideal for exposure to many data contexts and questions.
Long projects develop endurance, knowledge management, and the ability to shepherd requirements over time—ideal for depth and process robustness.

You can build both breadth and depth over time. The key, as Daniela puts it, is to voice what you enjoy, so project assignments align with your energy and strengths.

Changing teammates as a catalyst for method diversity

“You often work with different colleagues” implies a steady flow of new perspectives and working styles. Paying attention to how teammates structure data prep, form hypotheses, or run client conversations broadens your method repertoire. In settings like this, Daniela’s core advice—don’t try to know everything upfront, ask questions, and ease into the unknown—becomes a daily advantage.

The quiet core: steady growth without noise

What stands out in Daniela’s devstory is its calm clarity. No superlatives, no buzzword fireworks—just a practical account of work that creates value: prepare data so they become usable; analyze and model where it makes sense; talk to clients so needs and results meet; learn with colleagues by asking; and keep AI in view as a regular, not exceptional, part of the toolbox. It’s not flashy—it’s reliable.

For us, that’s the strongest message: competence grows organically when curiosity, openness, and teamwork line up.

Next steps for aspiring Data Scientists

If Daniela’s path resonates and you’re considering Data Science, you can start applying these ideas right away:

1) Map your curiosity

What questions do you naturally want to ask of data? Which signals or behaviors fascinate you?

2) Practice small end‑to‑end loops

Take a modest dataset and deliberately run the whole chain: ingest, clean, store, analyze, interpret. Don’t skip steps—link them.

3) Train for conversations

Practice explaining results clearly to someone outside the work. Simulate mini “client” discussions to refine how you translate methods into meaning.

4) Keep a living question log

Track recurring questions for data, stakeholders, and teammates. Maturity shows up as increasingly precise questions.

5) Seek feedback proactively

Ask colleagues for input on your prep steps, analysis choices, and result narratives. Feedback compresses the learning curve.

These steps strengthen the exact muscles Daniela spotlights: end‑to‑end thinking, curiosity, and open communication.

Conclusion: A grounded devstory with lasting takeaways

“Title: Daniela Böhm, Data Scientistin bei solvistas” isn’t a buzzword showcase. It’s a clear, actionable story from a Data Scientist who reached her field by following interest and now practices it as a holistic, communicative data process. Daniela highlights that variety—short and long projects, changing teams, responsibilities across the whole data chain—doesn’t overwhelm when two things are in place: curiosity and the willingness to ask questions.

That “AI is always part of it now” is something she treats matter‑of‑factly. That client conversations are core to the role is an opportunity for impact. And that you can “say what you enjoy,” shaping which projects you join, points to a work setting where team outcomes and individual strengths meet.

For us at DevJobs.at, the enduring takeaway goes beyond Data Science: technology wins when it speaks with people—with colleagues who answer when you ask and with clients whose data lead to better decisions. That’s the path Daniela Böhm lays out: from interest to competence—step by step, with curiosity and openness.

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