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Clarity

Lucian Lita

Sunnyvale, CA

I advise companies (startups and large organizations alike) with their data strategy, their data strategy (product, instrumentation, machine learning / AI), as well as auditing and designing their data architecture, data products, data science and analytics, and big data / distributed real time systems.

I love to build high performance technical teams, focused on delivering on product and platform and I can help you do the same in engineering, data science, and product. I also provide practical leadership advice on turning around large technical organizations, changing culture, and managing significant change, while building momentum and continuing to deliver.

$10.00/min

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Product Strategy Big Data Leadership Software Engineering Team Building Analytics Data Science Machine Learning Change Management Culture Building

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Big Data Architecture & Distributed Systems

I advise companies on big data architecture and on designing large scale platforms and services. The more / cheaper storage and computation choices we have, the more data we can process. However, many companies quickly become paralyzed when vast amounts of data are hurled at inadequate platforms. Designing solid and scalable architectures in-colo and in the cloud is critical for businesses to survive and thrive.

I've designed and built successful big data platforms and services for advertising, healthcare, financial services, and social networking - handling datasets of over 10 trillion transactions per month and tens to hundreds of thousands of dimensions. Many of these systems are real time or streaming and many provide off-line processing - when appropriate.

I care about optimizing the architectures *for the use cases they are supposed to solve* - i.e. falling in love with the problem, not the technology - and believe in short-term wins on the way to target state.

If you need advice on any of these topics, I'm happy to help.

Building Data Products

I advise and guide companies on data strategy and building data products - ranging from product design to data science and architecture.

The norm, especially for digital/data native users has become personalized experience, low friction, and a light weight flow to getting things done. This is true for users playing a game, doing their taxes, or for 3rd party developers using your APIs to build apps. To mind-meld with your users you need:
-- a constant data / feedback flow
-- frequent experimentation and adaptation, keeping a pulse on how the world changes
-- creating deeply personalized experiences
-- envisioning product features that are rooted in data (1st and 3rd party)
-- thinking data first (when you have that luxury), before any line of code has been written

Previously, I've designed and built data products in various verticals from financial services to social networking at IBM, Siemens, BlueKai, Level Up Analytics (and its many clients), and Intuit.

If you find you need advice and guidance in any of the areas above, I'm happy to help.

Data, ML, and AI Strategy

Until recently, having a data strategy was a luxury. Building machine learning and artificial intelligence capabilities was something only a handful of companies with dreamy founders would invest in.

That is no longer the case.

Today, it is crucial for companies have a strong, coherent data strategy in sync with your product and technology strategies. Data (collection, processing, analytics, predictive modeling, experimentation, personalization) as an afterthought typically leads to extreme pains as soon as products start to mature.

At Intuit (fintech), BlueKai (data advertising), Siemens (healthcare), and especially at Level Up Analytics (data sci & eng) I've helped very companies and startups in social networking, education, payments, mobile, fintech, hedge funds, etc. answer critical questions:
-- what do our users really want? how do we get our products and services there?
-- how do we establish a continuous user feedback stream, with security, privacy, and compliance in mind, while having the ability to learn and adapt our products quickly
-- how do we do personalization (that's easy)? how do we do personalization at scale (that's difficult)?
-- what ML and AI capabilities do we need? what 3rd party capabilities can we use so we can focus on building our distinct value and not reinvent the wheel?
-- how is our data different and how can we turn it into an asset?
-- can/should we monetize our data? How can we do that, while being a good corporate citizen.
-- building the ability to answer questions like these on an ongoing basis: why am I growing? Why am I not growing?
-- how do design the right data engineering and data science teams? What will the optimal org structure look like?
-- How do I hire the right talent to implement the data strategy? How do I align the incentives for the team to deliver in both short and long term?
-- How do I create and propagate a data mind-set, a data culture, and deep data thinking across the company?
-- How will ML & AI change my industry's landscape and how will it impact our company?

I'm happy to help you answer similar questions and setting up your data, ML, and AI strategy.

Building Engineering & Data Science Teams

Any company can build a data science and engineering team, but few end up with high performance teams that are focused and deliver.

Quite frequently leaders hire business analysts, research scientists, or engineers with basic predictive modeling knowledge for data science positions. Engineering teams often hire focusing mainly on technologies, rather on the ability to design well, implement effectively, and ship, ship, ship.

I love building technical teams for startups and large organizations alike, making sure the incentives are aligned, that there is clarity of purpose, a solid structure, and lightweight processes and tools that make sense.

In the past (at Siemens, BlueKai, Level Up Analytics, Intuit) I've built data science and analytics teams, big data and distributed systems engineering teams, and overall technical product organizations with a happy, effective, and coherent culture. I've done hundreds of interviews, founded a company, and have built technical teams at two successful startups (both acquired), and I'd be happy to help you build your team / organization.

Building High Performance End-to-End Technical Teams

Do you want to build high-performance teams in a large organization or a startup?

I love building technical teams for startups and large organizations alike, focused on the deliverables and getting the job done. I care about incentives being aligned, establishing clarity of purpose, a solid structure, and lightweight processes and tools that make sense.

This covers changing the way you design your team, the way you recruit, the way you interview, the way you set expectations for your team, the way you hire and fire. It is important to hire and grow the right leaders and set up the right incentives in order to scale, remove friction, innovate, and deliver (ship product, manage processes, set up infrastructure, etc.)

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Member since December 2016