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Principal Data Scientist

DescriptionFunction : Engineering, R&D → Data Science / Machine Learning / Operations ResearchAbout PulsePoint :PulsePoint is a fast-growing healthcare technology company (with adtech roots) using real-time data to transform healthcare. We help brands and agencies interpret the hard-to-read signals across the health journey and unify these digital determinants of health with real-world data to produce the most dimensional view of the customer. Our award-winning advertising platforms use machine learning and programmatic automation to seamlessly activate this data, making marketing, predictive analytics, and decision support easy and instantaneous.Principal Data ScientistAs a member of our Data Science Engineering team, the Principal Data Scientist will focus on optimizing real-time bidding strategies and auction mechanics to efficiently spend ad budgets and deliver against campaign targets.In addition to the above, you will work with the greater Data Science / Engineering teams on :• Improving existing solutions for page contextualization or developing new techniques;• Developing new or improving existing models of event predictions;• New feature engineering for multiple machine learning models :• Supply embeddings and / or segmentation, and supply quality metrics;• Fraud detection, etc.• Mining different data sources;• Supporting existing codebase for data integration and production support for our core models.Location : U.S. or EU (for EU candidates, we would expect to end days at 2-3pm EST)Requirements :Key Skills : Python, Algorithms, Optimisation, NLP, Data Mining, Statistical Analysis, Neural Networks, Generalised Linear Regression, Multiclass Classification, Java, R• Advanced knowledge of Python using standard DS packages (numpy / pandas / scikit, etc.); Being able to optimize and speed-up code.• 3+ years of RTB Auction or similar online technologies.In addition to the above, you'll need to have strong knowledge in the following areas :• Algorithms and Data Structures (e.g., sorting, search tree, binary heap, trie; time & mem complexities of algorithms)• Probability and Statistics (e.g., hypothesis testing; Markov process and its stationary distributions, stochastic matrix and its properties; Bayesian inference)• ML & DS (e.g., dimensionality reduction, geometry of PCA / SVD and of L1 / L2 regularisation, Decision trees and their ensembles, collaborative filtering, Thompson sampling / MCMC, Neural Networks, etc.)WHAT WE'LL GIVE TO YOU :• Comprehensive healthcare with medical, dental, and vision options, and 100%-paid life & disability insurance• 401(k) Match• Generous paid vacation and sick time• Paid parental leave & adoption assistance• Annual tuition assistance• Better Yourself Wellness program• Commuter benefits and commuting subsidy• Group volunteer opportunities, fitness challenges, and fun events• A referral bonus program we love hiring referrals here at PulsePointAnd there's a lot more!Follow us on Glassdoor to learn more about what it's like to work at PulsePoint!Watch this video here to learn more about our culture and get a sense of what it's like to work at PulsePoint!WebMD and its affiliates is an Equal Opportunity / Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.
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$140000 / YEARLY (est.)
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$120000K
$160000K

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What You Should Know About Principal Data Scientist, PulsePoint

If you’re a data enthusiast with a passion for healthcare technology, PulsePoint has the perfect opportunity for you as a Principal Data Scientist. Our company is at the forefront of transforming healthcare communication using real-time data, and you’ll be a critical part of our talented Data Science Engineering team in Minnesota. In this role, you will primarily focus on refining our real-time bidding strategies and auction mechanics, optimizing how we spend ad budgets to achieve campaign goals. Your creativity and analytical skills will come into play as you enhance existing solutions and develop new predictive models. Collaboration is key; you’ll work alongside other Data Science and Engineering teams to tackle challenges such as fraud detection and data mining from diverse sources. With proficiency in Python, algorithms, and statistical analysis, you will bring your advanced knowledge of machine learning and data mining to the table. PulsePoint is committed to its employees, offering comprehensive healthcare benefits, a generous vacation policy, and opportunities for professional growth through programs like annual tuition assistance. Join us and help shape the future of healthcare marketing while enjoying a vibrant company culture that promotes wellness, volunteerism, and personal development. We want you to thrive both professionally and personally at PulsePoint!

Frequently Asked Questions (FAQs) for Principal Data Scientist Role at PulsePoint
What are the responsibilities of a Principal Data Scientist at PulsePoint?

As a Principal Data Scientist at PulsePoint, you will be tasked with optimizing real-time bidding strategies and enhancing auction mechanics to maximize ad budget effectiveness. Your responsibilities will also include improving existing models, developing new predictive techniques, and supporting data integration for core models. This role requires effective collaboration with the greater Data Science team to innovate and enhance solutions.

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What qualifications are required for the Principal Data Scientist position at PulsePoint?

To qualify for the Principal Data Scientist role at PulsePoint, you should possess advanced knowledge of Python and relevant data science packages, along with a deep understanding of algorithms, statistical analysis, and machine learning techniques. Having 3+ years of experience in RTB Auction systems or similar technologies is essential, as well as familiarity with concepts like dimensionality reduction and neural networks.

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What skills are essential for a successful Principal Data Scientist at PulsePoint?

Essential skills for a Principal Data Scientist at PulsePoint include proficiency in Python, algorithms, data structures, and machine learning practices. Familiarity with NLP, data mining, and statistical analysis is also crucial. A strong analytical mindset integrated with the ability to optimize and enhance code speed is beneficial for excelling in this role.

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How does PulsePoint support the professional development of its Principal Data Scientists?

At PulsePoint, we believe in fostering personal and professional growth. Our Principal Data Scientists can benefit from our annual tuition assistance program, which encourages continuous learning and skill enhancement. Alongside this, we promote a culture of collaboration and innovation, providing ample opportunities for mentorship and project leadership.

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What is the work culture like for a Principal Data Scientist at PulsePoint?

PulsePoint fosters an inclusive and dynamic work culture focused on collaboration, innovation, and personal development. As a Principal Data Scientist, you'll be part of a team that values creativity and open communication, with numerous wellness programs, team-building activities, and opportunities to give back to the community.

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Common Interview Questions for Principal Data Scientist
Can you explain a real-time bidding model you have developed?

When discussing a real-time bidding model, detail the methodology you used, any algorithms implemented, and the results achieved. Highlight your ability to optimize performance and how your model addressed specific business goals.

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How do you approach feature engineering for machine learning models?

Describe your systematic approach to feature engineering, including the techniques you use to derive new features and how you assess their relevance and impact on model performance. Mention tools and frameworks you typically rely on.

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What statistical methods do you employ in your data analysis?

Mention the statistical methods you commonly apply, such as hypothesis testing or regression analysis. Provide examples of how these methods have driven insights in previous projects, focusing on practical applications.

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How do you ensure the integrity and quality of your data?

Explain your strategies for data validation, cleaning, and preprocessing. Discuss the importance of quality data in modeling and any specific tools or frameworks you leverage to maintain data integrity.

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Can you discuss a time when a project did not go as planned?

Share a specific example of a project that faced challenges. Focus on the lessons learned, adjustments made, and how you adapted your strategies in response to the situation.

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How do you stay updated with the latest trends in data science?

Outline your methods for staying informed about industry trends, literature, and new tools. Describe relevant blogs, conferences, online courses, or community involvement that contribute to your continuous learning.

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What is your experience with machine learning algorithms?

Discuss your familiarity with various machine learning algorithms, mentioning specific projects where you applied them. Highlight your ability to choose the right algorithms based on the problem context and performance metrics.

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How would you approach a new data science project at PulsePoint?

Describe your methodology for approaching new projects, including problem definition, data collection, analysis techniques, and stakeholder collaboration. Demonstrate your structured problem-solving skills.

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What tools do you commonly use for data analysis and modeling?

List the tools and frameworks you frequently use, such as Python libraries (e.g., scikit-learn, NumPy) or data visualization tools. Explain why you prefer them and how they enhance your workflow.

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How do you handle tight deadlines and high-pressure situations?

Provide examples of how you've effectively managed time and priorities in deadline-driven environments. Discuss your organizational strategies and techniques that enable you to remain calm and focused when under pressure.

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Revolutionizing health decisions through real-time data

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Full-time, hybrid
DATE POSTED
December 3, 2024

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