Harnham
SEARCH
Shortlist

Head of Product - ML / AI

San Francisco, California
US$200000 - US$220000 per year
Apply Save job

Head of Product - ML / AI

San Francisco Bay Area (open to remote)

$200k + Excellent Equity

This is a great opportunity to join a stealth-mode startup in the space of AI governance that are looking to hire a Head of Product. If you're excited by being a key player in the future of AI and leading the development of a comprehensive audit and governance platform then get in touch!

ROLE OVERVIEW - HEAD OF PRODUCT

  • Gain a deep understanding of customer experience, identify product gaps and generate new ideas that grow market share, improve customer experience and drive growth.
  • Lead the roadmap planning, backlog and sprints to ensure alignment Engineering and Design to deliver on the product roadmap.
  • Track industry trends and adapt our AI governance and Audit product strategy accordingly.
  • Lead the lighthouse customer proof of concept and deliver on the defined milestones.
  • Lead the customer panels to ensure that we are solving the right customer pain points.
  • Gather quantitative data (product analytics) and qualitative feedback (user interviews) to drive evidence-based product strategy and prioritization.
  • Build and develop a high-performing team of product managers while fostering a culture of growth, customer obsession, and intentional strategic thinking.

YOUR SKILLS AND EXPERIENCE

  • Masters Degree in Computer Science, AI, Machine Learning, or related field
  • 10+ years of experience in product management, providing product vision and roadmaps, leading product development - from PRDs to product launches.
  • Experience developing high growth B2B technology platforms and services.
  • Experience managing teams and working with cross-functional partners.
  • Experience as a product manager, engineer in machine learning and AI systems.
  • Experience in ethical AI, privacy, security, compliance, or areas related to improving overall trustworthy user experience when it comes to technology.

HOW TO APPLY

For more information about the role press "apply now".

KEYWORDS

Product Management, Product, Data, Platform, AI, ML, Machine Learning, NLP, Artificial Intelligence, Deep Learning, Governance, Risk, Audit, Data Governance, Responsible AI

Apply Save job
Send similar jobs by email
JP/CAIHOP
San Francisco, California
US$200000 - US$220000 per year
  1. Permanent
  2. Marketing Analyst

Similar Jobs

Director of Data Technology
Salary

US$180000 - US$200000 per year

Location

Austin, Texas

Description

This is a great opportunity to join an independent full-service digital agency as Director of Data Technology.

ADD TO SHORTLIST
Manager, Omnichannel Analytics
Salary

US$150000 - US$165000 per annum

Location

Dallas, Texas

Description

A globally-renowned Consumer Health Enterprise is looking to add a technically savvy Analytics professional to its newly formed Omnichannel division!

ADD TO SHORTLIST
Media Analytics Manager
Salary

US$120000 - US$130000 per annum

Location

Boston, Massachusetts

Description

Do you have a background in media and have proven commercial experience extracting insights from media mix and/or marketing mix models?

ADD TO SHORTLIST
Senior Data Analyst (Marketing)
Salary

US$400 - US$520 per day

Location

California

Description

Hiring for a senior data analyst to help produce insights for high level marketing stakeholders within this entertainment company.

ADD TO SHORTLIST

Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Amped Up Analytics: Google Analytics 4

Google Analytics 4 has amped up data insights into the behaviors and preferences of your customers. Where once each touchpoint only tracked what had been clicked, GA4 is bringing it all together in a more wholistic approach to the customer journey. As the fourth quarter of 2020 dawned, Google upped its game. Crafting a compelling array of features with machine learning at its core, this new platform offers a more customer-centric approach to data-driven insights, rather than split data across platforms and devices.   Though still in its infancy, there are some dramatic new changes afoot. And while it’s not a good idea to get rid of the old Universal Analytics platform before ringing in the new one, it is a good idea to understand what’s available now and what may come to be over time. Four Advantages to Google Analytics 4.0 From our desktop to our laptop to our smartphone, we carry our office in our pocket or on our lap. So, what better way to integrate what was once called “App + Web properties” into a more cohesive trackable measurement of data. Add to this the privacy protocols in place to protect customers, and Google Analytics 4 offers flexibility for future cookieless tracking and permissions, and advantages are revealed. Combined Data and Reporting Rather than focusing on one property (web or app) at a time, this platform allows marketers to track a customer’s journey more holistically.  The platform’s premise is that there is a pattern everyone follows. From the moment a customer visits your website to clicks on a button subscribing to your newsletter or blog – Acquisition and Engagement. To the moment your customer makes a purchase, is happy with the product or sevice, and comes back again – Monetization and Retention.  Designed for marketers who want to track users across multiple formats, Google Analytics 4 hopes to solve with Data Streams. These Data Streams merge to paint a picture of the customer journey from website visit to purchase. A Focus on Anonymized Data This anonymization answers the call to Data Privacy and third-party data collection. Crafting a unified user journey centered around machine learning to fill in any gaps, marketers and businesses have a way to get the information they need without diving into personal data issues. This is a key change in that Google is moving away from client-side focus and using server-side and customer-centric capabilities. With GDPR and privacy laws in full swing, marketers face enhanced privacy regulations as cookies are phased out or blocked. Predictive Metrics and Audiences Using Machine Learning to predict future transactions is a game changer for the platform. These predictive metrics for e-commerce sites on Google properties allow for targeted ads to visitors who seem most likely to make a purchase within one week of visiting the site.  Though focused on e-commerce sites now and based on transactions and revenue, there is an opportunity for marketers to identify and convert based on such leads as video views or form submissions. Machine Learning-Driven Insights The launch announcement for GA4 explains it “has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms.” Machine Learning-driven insights include details that elude human analysts.  What These Changes Mean on the Digital Frontier We’re all reaching for higher value and Google Analytics 4.0 brings it into one unified platform for the future. As we make the shift from traditional Google Analytics to its 4.0 version, there is opportunity to get more creative.   Wondering if you should upgrade? This article breaks down the pros and cons to help you decide.  If you’re interested in Big Data & Analytics, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Joshua Poore

Joshua Poore

08. April 2021

News

Making Sense of Unstructured Data with NLP

Natural Language Processing. It seems a simple enough explanation. The idea is to make computers sound like native speaking humans regardless of their language. Except there’s one problem. When we speak, we don’t follow our own rules of grammar. We use idioms, metaphors, abbreviations, and oftentimes use more body language to communicate than we realize.  So, what’s a poor machine to do when confronted with such an unstructured melee of data? Well, since semantics is not what you say it’s how you say it, we must teach computers to read between the lines. Of code. Enter NLP. The semantics of human language written for a machine to help make sense of our human behaviors gets organized. The Perfect Imperfections of Language Computers require structure. Natural language does not. Teaching machines how we communicate is no easy task, and yet we use machines that can do this every day. By combining technology and Machine Learning we begin to teach computers how to understand us. We teach them how to interpret and determine what it was we want done. When you’re asking Siri or Alexa a question, you’re helping them to learn how you ask, so they can better respond, and they make you more efficient. It’s a win-win for everyone. In business, using NLP techniques to drive business decisions is even more important. Now, the computer must decide what information is the most valuable to pull from a pile of Data. Understanding our choices, our tone, even the words we choose to use, helps our machines learn what we want to do or need done. Where is NLP Used? Since we use different rules when we speak than when we write, our computers learn how we talk and how to use language more naturally. Wondering where NLP might be used? In a word or two? Nearly everywhere. You are scheduling a meeting and when it’s time, a calendar reminder pops into your phone which says estimated drive time to the meeting based on traffic conditions in your area. Or you ask Alexa to play your favorite music list from Pandora.  Every touchpoint in this scenario is using NLP. We naturally might get into our car, ask our Virtual Assistant navigation system for directions, or to play our favorite music. Our choices don’t fit in a box and may not be logical, but the more we teach the machines, the closer they may get to understanding the nuances of our language. Here are 5 more ways we use NLP every day: Predictive text on your phone or in your Word document. Chatbots and Virtual Assistants to ensure customers are acknowledged in a timely manner, answer basic questions or redirect to appropriate personnel, and making suggestions to improve the customer experience.Curating social media feeds to determine customer needs and interest.Grammar correction software so our emails and business documents are error-free.Analyzing customer interactions using comments and reviews for customer feedback about a product or service. There’s a ton of information to be filtered, sorted, sifted, and analyzed, and NLP is just one of the tools Data Scientists use. Interested in the subfield of NLP? Check out this article for 6 techniques you need to know to get started. Already well-versed in the industry and looking for a new challenge? If you’re interested in Big Data and Analytics, Advanced Analytics, Life Sciences, Data Science, or any of our Data professional fields, we may have a role for you. Review our current vacancies or contact one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Elizabeth Sobel

Elizabeth Sobel

25. March 2021

News

Recently Viewed jobs