Florian Neukart of Terra Quantum argues that quantum machine learning is practical now, provided it is hybrid. Classical computers do what they are good at, quantum circuits are added only where they help, and the result is judged against the best classical solver. He ends on a security stack he calls "absolutely secure" against quantum attack, a room-temperature superconductivity result from Terra's own team, and an invitation to anyone with a machine learning background to start without a physics degree.
Hybrid rather than all-quantum. The host cites episode 721, where an earlier guest doubted near-term quantum ML. Neukart disagrees: early work shrank real problems into toy ones. Terra instead expresses parts of a network, such as a fully connected layer, as quantum circuits, stacked gates run on a gate-model machine. Its software maps those gates to whichever chip a customer picks, so switching hardware is a parameter change. Circuits must be shallow and touch few qubits at once, because coherence lasts nanoseconds to microseconds. Each added qubit doubles the configurations held in superposition. Because results are probabilistic, a circuit is measured perhaps a thousand times and the majority answer taken.
Applications. He groups uses into optimization, machine learning and simulation. Scheduling is the "boring" one he calls intractable classically: for Volkswagen, fitting 250-odd test classes to staff skills on cars leaving the line, and the same algorithm can schedule EV software subroutines to save power. Other cases are logistics (perishable goods at hubs within time windows, fewer empty miles), satellite constellations, collateral optimization and exotic options pricing. Simulation of molecules for batteries and drugs is the hardest, and he says it awaits stronger systems.
Hardware and Willow. Chip choice usually follows existing cloud contracts with AWS, IBM or others; IBM, Rigetti, D-Wave and Google are all in play. Better chips mean more and better qubits, longer coherence and more connectivity. Asked about Google's Willow, Neukart says even very high two-qubit fidelity compounds into random output at millions of qubits, and Willow showed errors can be held down while scaling. Engineering and cooling challenges remain.
Getting enterprise-ready. Terra benchmarks against CPLEX or Gurobi and says that if it is not better it makes no sense to use. New problems sometimes need new algorithms, which limits scale for a young, small field. Many firms want on-premises computing, but superconducting systems cost 15 to 20 million and date quickly. Neukart argues that data embedded as zeros and ones is meaningless without the encoding, which eases cloud worries. Terra offers Nvidia's hybrid quantum cloud among its backends and relies on GPU tensor networks, as in its chemistry package for conformer search. These can later be translated into quantum circuits.
Encryption. The host quotes Satya Nadella's billion-years-versus-two-minutes claim for RSA-2048 and expects no "encryption apocalypse". Neukart lays out post-quantum cryptography (NIST's recent three algorithms, safe "as of current understanding"), quantum key distribution over fibre, and quantum random number generation.
Ethics, superconductivity and the future. Terra is about to start self-funded clinical trials of laser treatment for osteoarthritis and arteriosclerosis, with parameters tuned by a hybrid quantum algorithm. It also published a fatty-liver classifier that beat the benchmark. Neukart says Terra's team around CTO Valery Vinokur achieved room-temperature superconductivity at ambient pressure in graphite. Designing qubits around it is still to come, and he cautions that earlier superconductivity discoveries did not deliver quickly. He is most excited by personalised drug design, then fusion and materials such as a space elevator.
This episode of the Data Neighbor podcast traces how VK, a former data journalist, became a data engineer at Meta. Its argument, made by guest and hosts alike, is that the path was found by trial: trying very different work, noticing what was enjoyable, and choosing the role that fit over the one with the most buzz. VK ends wanting to become a "full stack data professional" and holds that data engineers stay relevant by building AI's data foundation and using AI themselves.
From journalism to code. VK studied data journalism at the University of Missouri, partly because writing in English was hard to stand out in. The investigation was the part VK loved: one soybean-production story was picked up by the Associated Press. At the San Francisco startup Hoodline the data journalist title became "journalist engineer", generating neighbourhood news from geospatial and third-party data. VK describes a steep first year, learning coding and statistics from a manager and a mentor and by reading colleagues' code. The hosts draw the lesson that a transition is easiest inside your current company.
Finding the fit at Nextdoor. Nextdoor acquired Hoodline about six months after VK joined, and the team became a content data science team. Working on an ML image classifier alongside a pipeline-and-reporting project, VK felt the difference, was loaned to analytics engineering for six months, and stayed. VK defines analytics engineering as building the pipeline foundation and dashboards that enable analytics and ML, and equates it to data engineer at Meta and business intelligence engineer at Amazon. VK regrets not seeing this sooner, reckoning it cost at least two years, and notes the first promotion came only after the switch.
Moving for scale. As the analytics engineer on XP, Nextdoor's in-house experimentation platform, VK saw a prototype work in six months and maturity within a year, then left for Meta to work with larger data. VK credits Nextdoor's data science director with advice to find a role model for each career path.
Advice to newcomers. VK suits the job to people who enjoy cleaning data, organizing things and visualizing it. The suggested test is online SQL and Python courses, then LeetCode as a stand-in for real problems. Juniors are judged on execution and seniors on deciding what is worth doing. On AI, VK expects tooling to improve while the role must redefine its purpose around clean, real-time data foundations.
This Data Neighbor podcast episode follows a senior data scientist at Microsoft from a small town in Nepal through a 2009 recession, US military enlistment and federal data work into big tech. The guest's argument is that the detour was an asset: the military built resilience and leadership that he says take longer to learn than technical skills, while years as a cyber-defense analyst gave him the domain knowledge and customer focus that, in his account, matter more than model building. He ends on advice to new graduates: a personal roadmap, a mentor, and one subject learned fully before the next.
Getting to the US. The guest stayed in Nepal for a computer science degree at the Nepal College of Information Technology, turning down admission abroad because the undergraduate scholarship at home was larger and international students faced costs after 9/11. He came to the US in 2007 for a master's in information systems in upstate New York.
The 2009 decision. Graduating into high unemployment, he joined a job consultancy in New Jersey and left after about a week because it applied his resume to every role regardless of fit. With his OPT due to expire within a year, a friend on the subway passed him a recruiter's card for a temporary government program recruiting people with specialties; he was 60% to 70% decided on the 90-minute train ride home. His family's first answer was to come home. He signed around October 2009, kept the 30-day option to cancel, and started training in February 2010.
Training. Basic combat training was three months, from 4am to 10pm, with no phone and no weekends, required of every recruit whatever their job. The hardest part for him was mental: standing beside high-school graduates with a master's degree and wondering whether he was going backward. Five months of job training followed in supply and logistics, a role chosen because non-citizens could not work on computer data.
Preparing the exit. He built a Microsoft Access database and forms to replace his unit's Excel files and took a military-funded online MBA. Facing the choice to re-enlist, he saved three months of terminal leave and moved to San Diego, where VA-approved courses carried a housing benefit. Within a month he had a contract data analyst role with a US Navy cyber security unit, went full-time within about six months, and later was selected for a federal program funding a data science master's at the University of San Diego.
Data science at Microsoft. He now works on the security side as something like an internal consultant, building data science for teams with or without their own. He argues ML features come from human experience of the domain and that AutoML without subject-matter experts may not work. His hardest problem type is anomaly detection: tuning for recall creates false positives that erode trust, and benign positives from red teams complicate labelling.
This episode of the Data Neighbor podcast follows a guest who went from quantitative finance analyst to business analyst, business intelligence engineer, data scientist at Meta and, less than two months before recording, full-time entrepreneur. The conversation argues that such moves come from knowing your current priority, taking an imperfect first step, building skills on the job and promoting yourself loudly, supported by confidence and relationships that formal career ladders never mention. It ends with the guest's interview checklist and a step-by-step route into data for people outside it.
From finance to a foot in the door. The guest studied industrial engineering at UC Berkeley and, as an international student, needed any job that would sponsor a visa; about 40 interviews yielded one offer, at Bank of America. A junior-year internship at Facebook had ended without a return offer, which the guest blames on imposter syndrome: almost silence in meetings, no requests for help, and no relationships with the manager or team. The finance job took the guest to Charlotte, where they danced as an NFL cheerleader at the Super Bowl, an experience they would not trade for the millionaire-by-30 path a return offer might have brought. After failed interviews about two years out of college and rejections for five to seven data science roles a year later, the guest took an Amazon business analyst job at a 40% pay cut, which they say paid off in the long run.
Knowing your metric. A co-host frames this as knowing the metric you are optimizing, whether compensation, entry to a field or management. The guest agrees priorities shift every few years: tech at all costs then, compensation first four or five years later, once in tech with bargaining power.
Building a promotion case. At Amazon the guest found the job was mostly writing simple SQL pulls, pitched a self-serve tool to the director, and built it with an AWS database, pipelines and an Excel/VBA front end using free AWS credits. That became the evidence in a push to BIE, backed by a supportive manager; later docs arguing for headcount turned the side project into a team of five BIEs. Talking to product managers filled the product gap, and Meta made an offer on the third attempt. The hosts call this being "noisy"; the host describes brag documents at Stripe, the guest says Meta's performance reviews worked the same way, and the host says his staff promotion depended on people beyond his manager knowing him.
Confidence and superpowers. The host recalls staying quiet in consulting meetings and credits managers who amplified his work. A co-host describes feeling out of place among PhDs until owning executive storytelling as a superpower. The guest argues creativity matters more as standard problems are automated. Confidence lessons follow: a mentor's "confidence of a man", a hedged statistic that women apply at 90% of qualifications and men at 50%, and "no one here knows what they're doing."
Work-life balance. The host says long hours at Nextdoor doubled his salary, while consulting gave top reviews but no promotion or raise. A co-host argues balance matters only when work is not for yourself, and the guest says the best managers were the hardest critics. Their top lessons were raising the return on time: working on important problems, productionizing pipelines, getting feedback early and learning to say no.
Going solo. The guest left a full-time role to test a hypothesis that AI makes one-person businesses easier to start and run, supported by family, a partner and a friend who run businesses, and some savings; they caution the story may differ a year on.
How to get hired. As a coach, the guest ranks communication first, then SQL beyond advanced practice problems, then a short list of statistics applied well, then mock interviews, with large gains over about 10 sessions. Analysts should transfer internally, use referrals and keep resumes simple with quantified impact. Outsiders should master Excel or SQL before Python, build Kaggle projects from a list of 10 industries, volunteer on real data, and persist: the guest cites people applying to 6,000 jobs for 11 interviews and says their own route took four years.
This Data Neighbor Podcast episode interviews Karthik, a senior machine learning engineer at Meta who previously worked at Amazon and interned at Google, and argues through his story that a move into machine learning is a sequence of deliberate, adjacent steps rather than a lucky break. He ends on two claims: that his apparent confidence is imposter syndrome turned into a habit of seeking out smarter people, and that skydiving taught him every problem is solvable.
Getting in the door. Karthik's interest began around 2013 or 2014 with Google search and the PageRank algorithm, which led him to data mining and machine learning courses. From Jaipur he cold-emailed alumni and professors; he estimates 99% never answered. He interned with a professor at another Indian university to find contacts, and eventually a Minnesota alum's lab took him on, which led to a master's at the University of Minnesota in 2015. He calls himself an introvert, and credits a mentor's line that the worst case is a "no" that leaves you where you started. The hosts' reading is that what looks like luck was a 1% chance worked for relentlessly.
The detour through software engineering. A student loan, a visa clock of a few months and the dominance of PhDs in ML hiring at the time pushed him toward software engineering. He chose an Amazon pricing team next to an ML team over a better-paid Google Android offer, citing Bezos's regret-minimization framework. Once there he kept asking his manager for ML work, and later declined a fast track to senior software engineer in about four years, taking smaller ML projects instead because a senior-level switch would carry higher expectations.
Building the path. He contributed a feature the scientists lacked the bandwidth to build; it went into the next model iteration and built the team's trust in him. His route ran through ML infrastructure (an inference platform for dynamic pricing), feature engineering, ML engineering, search within Amazon, and then Meta. He says most software engineers who move to ML follow this route because infrastructure skills transfer. For someone outside tech, such as in healthcare, he stresses domain knowledge as the hardest part to earn, then coding and a network of practitioners; the hosts frame it as a series of bridges, and Karthik adds that some people have to switch companies to make a jump.
What the job is. He describes ML engineering as a spectrum from infrastructure to research, with titles that differ by company and team. As an applied engineer he owns search and search ads: diagnosing why some queries fail, then choosing between a heuristic, an optimization or a model, and evaluating through A/B tests.
Confidence and perspective. Karthik says he struggles with imposter syndrome at every new team, and would again in a field such as computer vision, and that it pushes him to find people smarter than him. He took up skydiving in 2020 and is now licensed. He says seeing the world from 13,000 ft eased his anxiety, and he recommends a tandem jump to everyone, putting the risk of death at under 0.001%.
In this Data Neighbor Podcast episode, two of the three co-hosts (the channel owner is absent) argue that impact is any work that changed something useful to the company — a metric, a roadmap or a process — and that data people lose credit for it mainly by never following up. They close on a recipe for any data role: track it, follow up, and state it concisely.
What impact is not. The co-hosts open with counter-examples: a cutting-edge tool such as XGBoost or an LLM used on a problem with no business return, and an ad hoc request answered and handed off with a "thank you", after which nothing is heard. The second may have had impact, but impact you do not know about cannot go on a resume or into a recruiter screen.
Three kinds of impact. One co-host's framework: moving a metric through features built on your insight, changing a roadmap (an analysis that shuts down a hopeless business line), or changing a process (a one-click experiment-analysis tool). The other co-host maps these to revenue gained, money saved, and productivity, which is the most indirect link.
Mindset. The co-host who manages a data-science team says they learned about impact only when recruiters asked for numbers. Speaking about it gets you more of that work, and an engineering director's puzzle analogy holds that the insight is the piece without which there is no impact. Credit is shared, weighted among contributors, not split hair by hair.
Tactics by role. Follow up with PMs and engineers on experiment ramps and results, document them, and book a 15-minute check-in the next week. Analysts describe dashboards by scope and audience, as in their example line: a company-level dashboard used daily by executives with over 500 views. Product data scientists cite the roadmap and metric movement, ML engineers the model and the metric, and experimentation or research teams the company-wide process change.
The first episode of the Data Neighbor Podcast, hosted by three former Nextdoor colleagues, argues that there is no single route into data work: a break snowballs for whoever has prepared for it, and the field's titles are messier than its few underlying families.
Why the podcast. The hosts frame it as demystifying data science, a job that means something different at every company, for people without friends in the industry. They promise weekly episodes, guests from ICs to VPs, and a coming episode on why there are so many data roles.
Three routes in. One co-host went from computer science and SQL-heavy software work to a business-school master's, six years at Microsoft, then eBay and Nextdoor, and is moving to Grammarly as a senior manager. Shane went from policy and the Peace Corps through PwC and AppFolio to senior data scientist at Stripe. The third studied statistics, skipped the actuary path, and went through gaming to Facebook, Pinterest, LinkedIn and director at Nextdoor.
Luck and openness. The hosts argue that one break is all it takes and that you raise your "luck surface area" by working on yourself, citing a top-10% resume list and certifications behind a Microsoft offer. Shane adds that people should try different data roles and company sizes, because the skills transfer.
Titles. A host says the field's hundreds of titles fall into five or six families and need a cleanup. Examples include a Microsoft title of "applied statistics engineer", and an argument on LinkedIn over whether causal inference counts as data science.
In this Data Neighbor Podcast episode the guest, a senior director of data science at Instacart who previously led a data-science team at Facebook, argues that breaking in takes preparing for luck and then exploiting it hard. He lays out two of the title's five steps (skip prestige targets, and invest about two hours in each application) and ends on the view that AI will make data scientists more productive rather than replace them.
His path. He studied mechanical engineering, found it dull, and took a master's in engineering management at Duke. That led to a business analyst job at Capital One in 2012, doing modeling and experiments before the data-scientist title existed there. Bored by credit cards, he applied widely in tech and Google called back six months later; the move was technically a demotion from L4 to L3, which he did not notice at the time. In 2016 Facebook turned its product analysts into data scientists, and he joined as an L4. He stayed almost six years in one seat, growing it into a team of about 25 with managers of managers, before leaving for Instacart about two and a half years before the recording.
Luck. He splits luck into a before and an after: put yourself in position, then go flat out when it lands. His highway analogy says that when the traffic clears you floor it, and he holds that someone whose area becomes the CEO's focus should work 12-hour days. Interviews are random too, he says, and a defeated or entitled attitude shows through to recruiters in a market he calls the worst he has seen for new grads.
The two steps. First, avoid big-name companies and titles without a connection, and look locally for analytical roles. Second, judge success by offers, not applications: spend about two hours each, tailoring the resume and finding connections, because his junior postings draw hundreds of LinkedIn messages and low-thousands of applications. A host adds that applicants should ask what they can give. The guest says that could be the good feeling of helping, a specific question about the person's work, public bug-finding, or open-source work. A host who once analyzed LinkedIn's job-seeker data recalls referrals being roughly 50x as effective as blind applying. The guest's tip is to interview as though talking to a coworker.
Discovering a strength. At Facebook he was asked to lead a monthly business review with the head of News Feed when the usual presenter was out, found he was good at telling data stories, and built it into a staple of how the business was run. That gave him direct time with leaders. His rules: do the chewing for executives, and make a deck clear enough that a seven-year-old could explain it to another, because executives retell it. A host adds the rule of cutting any artifact in half twice.
AI. The guest calls the claim that AI will replace data scientists untrue, likening AI to unlimited intelligent college interns. He says Instacart's data scientists each have 1.5 to 1.8 people's worth of work, so help from AI gets more done rather than eliminating jobs. He uses it for writing — shortening and polishing in his own style after feeding it samples of his writing — and for learning unfamiliar domains such as marketing, but finds its behavioral-interview answers canned. A co-host warns that AI-written Slack messages are obvious.