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Principal Machine Learning Data Scientist

About Citizen Citizen is the No. 1 public safety app in the U.S., with a mission to make the world a safer place. Citizen provides 911 alerts so people can use their phones to keep themselves, and the people and places they love, safe. Citizen has notified people to evacuate burning buildings, deterred school buses from nearby terrorist attacks, and led to the rescue of kidnapped children and missing people.Citizen’s 911 alerts are accompanied by live stories, real-time updates, and user-generated content so app users never have to wonder why there are helicopters overhead or fire engines flying by. By broadcasting from the scene of an incident, communicating with one another, and reading live updates, communities are empowered by Citizen. We act fast, break news, and give people the immediate information they need to stay safe. And we’re just getting started.Our flagship paid product—Citizen Premium—is a only-of-its-kind personal safety subscription that allows users to reach a digital guardian 24/7 for $20/mo. Subscribers used Citizen Premium to guide emergency response to remote hiking locations, or travel safely on late-night walks.Citizen Plus, which sits between the free Citizen app and Citizen Premium at $6/mo, currently unlocks 6 new features. Available for the first time ever, Citizen Plus includes the powerful ability to listen to police and fire radios, customize your alerts, unlock past incidents, create alert zones, view nearby registered offenders, and see crime statistics.Already relied on by millions of people every day, Citizen will expand even further across the United States this year to keep more users safe and informed. We’re looking for hardworking, mission-driven individuals to help bring Citizen to hundreds of cities nationwide.Citizen is backed by 8VC, Founders Fund, Goodwater Capital, and Greycroft and has raised $100M+ in VC funding.About the Role:We are seeking an experienced Data Scientist, Machine Learning to lead initiatives and drive the development of key, net-new features. As the lead ML expert with analytics capabilities, you will be responsible for spearheading projects such as real-time audio transcription, relevance detection, and the conversion of unstructured information into structured content. Your expertise will guide prioritization and impact analysis across ML initiatives, while also identifying opportunities to leverage new AI/ML tooling within our workflow. Furthermore, you will play a pivotal role in enhancing the rigor and accuracy of our predictive analytics, enabling us to predict users who are heading towards significant events, such as converting to paid plans or churning. If you are a proactive and innovative individual with a passion for both analytics and machine learning and a track record of delivering impactful solutions, we invite you to join our team and help shape the future of our organization.Our Challenges:• Scaling: We have one of the fastest-growing organic user bases in key metropolitan areas, and have expanded to multiple other cities. We are focused on the nationwide launch and the need to support that scale.• Bursting: We designed our infrastructure to scale without notice in case of a spontaneous incident where we need to inform our entire user base. On significant events, we see over a million simultaneously connected clients and their associated live streams. The core systems need to be able to efficiently support these traffic patterns and continue to scale to millions of more users in the future.• Machine learning: We process thousands of hours of audio every day looking for incidents that impact our users’ safety. To do this at scale, we plan to build ML models for audio analysis and targeting using the current state of the art from academia.• Analytics: We want to alert users to the incidents that matter to them, in a way that scales across different geographic densities and demographics.• Mobile video streaming: Our app will ingest high-quality video at low-latency, transcode, and redistribute the video to external media outlets seamlessly.Our Stack - languages and technologies we use and teach• Services: Go for transactional systems; Python for analytical systems• Datastores: Cassandra, MySQL, Redis, Google PubSub• Infrastructure: Kubernetes on Google CloudPreferred Qualifications• Computer Science degree or Machine Learning related degree; or equivalent work experience in the field• Good theoretical grounding in core Machine Learning concepts and techniques• Experience with a number of ML techniques and frameworks, e.g. data discretization, normalization, sampling, linear regression, decision trees, SVMs, deep neural networks, etc• 3+ years experience leading and delivering effective ML solutions for production use casesSalary Range & Benefits:The below represents the expected salary range for this position in New York, New York. We take a number of factors into account when determining compensation including your location, experience, and other job-related factors.Salary Range: $160,000-$205,000 annually + equity + benefitsCitizen offers a competitive benefits package including medical, dental, vision, flexible spending accounts, paid time off, company holidays, stock option plan, commuter benefits, and various wellness perks.Citizen is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class.
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What You Should Know About Principal Machine Learning Data Scientist, MITRE

Citizen, the leading public safety app in the U.S., is on the lookout for a talented Principal Machine Learning Data Scientist to join our innovative team in Bedford, MA. This is an incredible opportunity to work alongside passionate individuals dedicated to making the world safer through advanced technology. As the Principal Machine Learning Data Scientist, you'll spearhead crucial initiatives and develop groundbreaking features that enhance our platform. Your role will involve utilizing state-of-the-art machine learning techniques to analyze audio data, improve predictive analytics, and convert unstructured information into structured content. With the responsibility of prioritizing ML projects and leveraging new AI tools, your efforts will have a significant impact on our growing user base. If you’re passionate about using your data science skills to promote public safety and have experience in delivering effective ML solutions, we can’t wait to meet you. Join us at Citizen and help transform how communities stay informed and safe. With a competitive salary and a commitment to employee well-being, we provide an environment where innovation thrives and your contributions make a difference.

Frequently Asked Questions (FAQs) for Principal Machine Learning Data Scientist Role at MITRE
What responsibilities will the Principal Machine Learning Data Scientist have at Citizen?

The Principal Machine Learning Data Scientist at Citizen will take on various responsibilities, including leading the development of new machine learning features, conducting impactful analysis on projects such as real-time audio transcription, and transforming unstructured data into usable formats. Additionally, you will focus on enhancing predictive analytics to forecast user behavior, ensuring our services continuously meet the high demands of our fast-growing community.

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What qualifications are needed for the Principal Machine Learning Data Scientist position at Citizen?

To qualify for the Principal Machine Learning Data Scientist role at Citizen, candidates should ideally possess a degree in Computer Science or a related field with a strong background in machine learning concepts and techniques. At least 3 years of experience in leading machine learning projects for production use cases is also essential. Familiarity with various ML frameworks and techniques will further strengthen your application.

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How does Citizen implement machine learning in its operations?

Citizen employs machine learning to process vast amounts of audio daily, detecting incidents that could affect user safety. By building machine learning models for audio analysis and leveraging advanced analytics, Citizen ensures users receive timely alerts about incidents that matter to them. This approach is crucial for maintaining the safety and well-being of our growing user base.

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What is the work culture like for the Principal Machine Learning Data Scientist at Citizen?

At Citizen, the work culture is driven by a mission to enhance public safety through innovative technology. Being part of a dedicated team where your contributions truly matter is a key aspect of our environment. We encourage creativity and proactive thinking, allowing our employees to actively participate in shaping the future of the platform while working collaboratively with other talented professionals in the field.

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What salary range can one expect as a Principal Machine Learning Data Scientist at Citizen?

The salary range for the Principal Machine Learning Data Scientist position at Citizen is between $160,000 and $205,000 annually. Compensation can vary based on factors such as location, experience, and job-related qualifications, along with equity options and a comprehensive benefits package to support employee wellness and satisfaction.

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Common Interview Questions for Principal Machine Learning Data Scientist
What machine learning techniques have you successfully implemented in previous projects?

In answering this question, highlight specific machine learning techniques you've utilized, such as decision trees, neural networks, or clustering algorithms. Discuss a particular project where you applied these techniques effectively, the challenges you faced, and the outcomes achieved. Providing quantifiable results can bolster your answer.

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Can you describe your experience with real-time data processing in ML applications?

For this question, share experiences where you developed machine learning solutions that functioned in real-time. Discuss the frameworks and tools you used, how you managed data influx, and how your solutions improved decision-making or efficiency in practical applications. Mention any specific metrics that demonstrate the project’s success.

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How do you approach the problem of model overfitting?

To address overfitting, I employ techniques such as cross-validation, regularization methods, and pruning in decision trees. It’s important to explain how you monitor model performance on training versus validation datasets and the strategies you use to ensure your models generalize well to new data.

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What experience do you have with audio data processing?

Share specific examples of working with audio data, including the techniques you've applied such as feature extraction or noise reduction. Discuss any projects where you built models that analyzed audio for classification or incident detection and what frameworks you used, such as TensorFlow or PyTorch.

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How do you prioritize projects when multiple machine learning initiatives arise?

Explain your criteria for project prioritization—such as impact on user safety, scalability, and resource availability. Discuss a past scenario where you had to balance priorities and the approach you took to ensure key initiatives received the necessary attention and support.

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Can you explain a time when a machine learning project did not go as planned?

Acknowledge the importance of failures in the learning process. Describe the project, what went wrong, what lessons were learned, and how you adapted or pivoted as a result. This will demonstrate your resilience and ability to learn from setbacks.

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What strategies do you use for feature selection in your ML projects?

Discuss techniques like LASSO, Recursive Feature Elimination, or using domain knowledge to identify and select features that contribute the most to model performance. Providing a concrete example of a project where feature selection was key to your model's effectiveness can strengthen your answer.

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What role does data preprocessing play in your machine learning workflow?

Emphasize that data preprocessing is crucial for quality model training, covering aspects like data cleaning, normalization, and transformation. Discuss specific preprocessing steps you’ve implemented in past projects and how they influenced the outcomes.

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How do you evaluate the success of your machine learning models?

Explain your approach to model evaluation, which might include metrics like accuracy, precision, recall, F1 score, or AUC-ROC. Discuss real-world applications of these metrics and why they are important for ensuring the effectiveness of the ML solution deployed.

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What do you think are the top trends in machine learning right now?

Share insights on current trends like the rise of automated machine learning (AutoML), increased use of federated learning for privacy protection, or advancements in AI explainability. Relating these trends to Citizen's mission of improving public safety can also demonstrate your alignment with the company's goals.

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We’re a not-for-profit company powered by our mission: Solving problems for a safer world. We discover. We create. We lead. All for the public good.

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Full-time, on-site
DATE POSTED
December 5, 2024

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