Senior Manager Applied Scientist
About Interos
Interos is the supply chain risk intelligence company - building the most trusted and transparent supply chains in the world. Our pioneering discovery and monitoring intelligence spans the lifecycle of supply chain risk, enabling faster and more informed threat mitigation. As the world's first, and only, automated supplier intelligence platform, we continuously map and monitor extended supply chains at speed and scale to protect organizations from regulatory fines, unethical labor, cyber-attacks, and other systemic vulnerabilities. Interos serves a variety of commercial, government, and public sector customers around the world including a host of Global Fortune 500 companies and from within the members of the Five Eyes nations. www.interos.ai.
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The Opportunity
We are seeking a Senior Manager Applied Scientist with an exceptional product mindset to lead the development of innovative data-driven products that provide our customers with actionable insights into their supply chain risks. This role requires expertise in applied scientific methods, data analysis, and the translation of complex data into products that drive value for our customers. The ideal candidate will have a track record of developing applied scientific solutions, including statistical modeling, data visualization, and data transformations, to deliver practical, customer-centric results.
At Interos, the Applied AI team is dedicated to researching and applying new techniques to communicate supply chain risks. We collaborate closely across product, engineering, and research teams to anticipate customer needs, explore diverse datasets, and create data products that integrate seamlessly into our platform.
Essential Functions/Duties:
- Provide leadership and guidance to a team of applied scientists, initially managing 1-3 direct reports with potential for team expansion.
- Take an applied, hands-on approach to working with complex data sets, generating solutions that drive product development (e.g., visualizations, transformations, statistical models) that create real-world value.
- Collaborate with cross-functional teams (data engineers, software engineers, product managers) to take models and algorithms from concept to production.
- Focus on the applied side of scientific research, ensuring that products are developed with a focus on real-world applications, usability, and impact, rather than pure theoretical research.
- Ensure data security, privacy, and quality considerations are fully integrated into product design and model development.
- Work with large, complex datasets to assess and improve their quality and usability for applied data products.
- Contribute to thought leadership through publications, presentations, and other forms of knowledge-sharing with both technical and non-technical audiences.
- Help refine and improve data-driven processes, applying scientific techniques and methodologies to solve business challenges.
- Engage with customers, both directly and indirectly, to better understand their needs and ensure solutions are aligned with their business goals.
Minimum Qualifications:
- PhD or similar level experience on the job
- At least 8 years of experience in applied data science, with a focus on solving real-world problems rather than purely theoretical research.
- Strong proficiency in scientific programming, including Python and relevant data manipulation libraries (e.g., Pandas, NumPy), as well as experience with scientific computing and statistical modeling.
- Experience developing data-driven products and understanding the process of taking solutions from prototype to production.
- Broad range of AI/ML applied modeling backgrounds - GenAI, NLP, GitLab, Jira, data pipelines, APIs
- A collaborative and open communication style, with the ability to work in a distributed team and engage with cross-functional stakeholders.
- Proven ability to translate complex scientific concepts into practical, actionable insights for non-technical audiences.
- A demonstrated track record of leadership, including growing teams and mentoring junior scientists.
- Min Years of Relevant Experience w/Degree: 8
- Minimum Degree Requirement: Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or other quantitative field and 15 years of related experience; or 12 years of related experience and a Master's degree; or 8 years of related experience and a PhD
Preferred Qualifications:
- PhD in Computer Science, Applied Mathematics, Statistics, or a related quantitative field, or equivalent work experience.
- Experience with Natural Language Processing (NLP), Time Series Analysis, Knowledge Graphs, Graph Theory, or Network Analysis.
- Familiarity with data pipelines, APIs, and data integration processes.
- Expertise in data visualization and the ability to create compelling visual representations of complex scientific results.
- Familiarity with relevant open source tools and libraries in applied science (e.g., Jupyter, Scikit-learn, etc.).
- Startup experience or similar, with a focus on delivering impactful, executable solutions in fast-paced, evolving environments.
Reports to: SVP, Research and Analysis
Location: Virtual (USA)
Supervisory Responsibility: This position will have supervisory responsibilities
Years of Supervisory Exp. Required: 1
Travel Requirements: This position does not require travel.
Telecommute Option: Yes
Work Environment: This job operates in a professional office environment. This role routinely uses standard office equipment such as computers, phones, printers.
Physical Demands: This is largely a sedentary role. Physical requirements include occasional lifting/carrying of 5 pounds; visual acuity, speech and hearing; hand and eye coordination and manual dexterity necessary to operate a computer keyboard and basic office equipment. Subject to sitting, standing, reaching, walking, twisting, and kneeling to perform the essential functions. Working conditions are primarily inside an office environment.
Compensation range is base salary of $131,250 -$218,750. The salary range information provided, reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual salary will be commensurate with skills, experience, certifications or licenses and other relevant factors. In addition, this role will be eligible to participate in either the annual performance bonus or commission program, determined by the nature of the position.
FLSA Status: Exempt
Benefits
Comprehensive Health & Wellness package (Medical, Dental and Vision)
10 Paid Holiday Days Off
Flexible Time Off (FTO)
401(k) Employer Matching
Stock Options
Career advancement opportunities
Casual Dress
On-site gym and dedicated Peloton room at headquarters
Company Events (Sports Games, Fitness Competitions, Birthday Celebrations, Contests, Happy Hours)
Annual company party
Employee Referral Program
Notice: Be Cautious of Employment Scams!
Please be aware that there are individuals and organizations that may attempt to scam job seekers by offering fraudulent employment opportunities in the name of Interos. These scams may involve fake job postings, unsolicited emails, or messages claiming to be from our recruiters or hiring managers. Please note that Interos will never ask for any personal account information, such as cell phone, credit card details or bank account numbers, during the recruitment process. Additionally, Interos will never send you a check for any equipment prior to employment.
All communication from our recruiters and hiring managers will come from official company email addresses (@interos.ai) or from Paycor (sometimes coming through as "Newton," a subsidiary). We will never ask for any payment, fees, or purchases to be made by the job seeker, and our interviews are conducted via phone calls and on-camera video meetings (not text-based messaging). If you are contacted by anyone claiming to represent Interos and you are unsure of their authenticity, please do not provide any personal/financial information and contact us immediately at hr@interos.ai.