Data Scientist, Cobalt
$100-130k
+ Equity
SQL
Python
Tableau
R
Power BI
NumPy
Pandas
Scikit-Learn
Git
Mid and Senior level
Remote from US
More information about location
Penetration testing software provider
Be an early applicant
Penetration testing software provider
201-500 employees
B2BSecuritySaaSCyber Security
Be an early applicant
$100-130k
+ Equity
SQL
Python
Tableau
R
Power BI
NumPy
Pandas
Scikit-Learn
Git
Mid and Senior level
Remote from US
More information about location
201-500 employees
B2BSecuritySaaSCyber Security
Company mission
To become the world's interface to the security workforce.
Role
Who you are
- This role presents an excellent opportunity for a proactive and analytical Data Scientist with approximately three years of professional experience, eager to apply and expand their expertise in a dynamic, data-driven environment
- A minimum of 3+ years of demonstrable professional experience in a Data Scientist, Data Analyst, or a closely related quantitative role. This experience should include a proven track record of applying analytical techniques to solve real-world problems. Additionally, experience working with large, diverse datasets and contributing to data-driven projects from their conception to the delivery of insights is essential
- Exceptional ability to dissect complex business problems, identify underlying causes, and develop innovative, data-driven solutions
- Excellent verbal and written communication skills are essential, with the proven ability to effectively convey complex technical concepts and data insights to diverse audiences, including non-technical business stakeholders and senior leadership
- Demonstrated ability to work effectively and contribute positively within a collaborative team environment, fostering open communication, sharing knowledge, and supporting collective goals
- Meticulous attention to detail and a strong commitment to ensuring data accuracy, quality, and the robustness of analytical outputs
- Proficiency in Programming Languages: A strong command and hands-on proficiency in Python (including key libraries such as Pandas, NumPy, and Scikit-learn) and/or R are necessary for data manipulation, statistical analysis, and machine learning model development
- Database Management & SQL: Expert-level proficiency in SQL is required for efficient querying, extraction, and management of data from relational databases. Familiarity with database concepts and schemas is also essential
- Statistical Analysis & Modeling: A solid foundational understanding and practical application of statistical concepts, including hypothesis testing, A/B testing, regression analysis, probability distributions, and statistical significance, are crucial for data analysis and model building
- Machine Learning Algorithms: Hands-on experience with a variety of supervised and unsupervised machine learning algorithms, such as linear/logistic regression, decision trees, random forest, gradient boosting, and clustering, and their practical application in solving business problems is expected
- Data Visualization Tools: Demonstrated experience with data visualization libraries (e.g., Matplotlib, Seaborn, ggplot2) and/or business intelligence tools like Tableau or Power BI is necessary to create clear, insightful, and impactful reports and dashboards
- Version Control: Familiarity and practical experience with version control systems, particularly Git, are required for collaborative coding, code management, and ensuring reproducibility of work
What the job involves
- The individual will serve as a key contributor to our data science initiatives, working closely with the Director of Data Science, senior data scientists, and various cross-functional teams
- The primary objective will be to transform complex raw data into actionable insights that directly drive strategic business decisions and improve operational efficiency
- This position offers significant opportunities for hands-on machine learning model development, in-depth data analysis, and active participation in the full lifecycle of data science projects
- All of these contributions will be made while benefiting from direct mentorship and a clear professional growth path within the team
- The Data Scientist will systematically gather, clean, and preprocess large, often complex and varied, datasets from diverse primary and secondary sources. This ensures high data quality, consistency, and integrity, which is fundamental for subsequent analysis and accurate model building
- The role requires implementing robust data wrangling techniques, including identifying and handling missing values, outliers, and inconsistencies, to transform raw data into a suitable and optimized format for analytical and modeling purposes
- This foundational work is critical, as the reliability of any subsequent analysis or model hinges on the quality of the input data
- The individual will conduct thorough exploratory data analysis to uncover hidden patterns, significant trends, and critical anomalies within datasets, leading to the generation of insightful hypotheses
- This involves applying appropriate statistical tools and rigorous techniques to validate these hypotheses, derive actionable conclusions, and inform data-driven decision-making processes across various business functions. The emphasis here is on translating raw data into meaningful observations that can guide strategic choices
- A core responsibility involves collaborating effectively with the Director of Data Science and senior data scientists to design, develop, train, and fine-tune various machine learning models
- These models will address tasks such as classification, regression, clustering, and forecasting. The Data Scientist will also assist in the seamless implementation and deployment of these predictive and statistical models into production environments, with a focus on ensuring their scalability, efficiency, and reliability
- This includes designing and executing experiments to rigorously test model performance, optimize hyperparameters, and select the most appropriate algorithms to maximize accuracy and efficiency
- The Data Scientist will be responsible for creating clear, compelling, and intuitive data visualizations, interactive dashboards, and detailed reports. These outputs are essential for effectively communicating complex findings and insights to both technical and non-technical stakeholders
- A key aspect of this responsibility is the ability to translate sophisticated technical results and statistical concepts into clear, concise, and actionable recommendations that can be readily understood and utilized by business teams
- This role requires working in close partnership with data engineers to meticulously define data requirements, construct robust data pipelines, and ensure the necessary infrastructure and tools are in place to support model training and evaluation
- The Data Scientist will also actively engage with cross-functional teams, such as product, marketing, and operations, and business stakeholders to deeply understand their challenges, gather detailed requirements, and integrate data insights seamlessly into broader business strategies
- Proactive participation in team meetings, presenting project progress, and discussing findings will ensure that data science efforts are consistently aligned with overarching organizational objectives
- Under the direct guidance and oversight of the Director of Data Science, the Data Scientist will contribute to the establishment and promotion of best practices in data quality, experimental design, and model reproducibility. T
- The role also involves proactively identifying and proposing opportunities for automation and optimization of existing data processes to enhance efficiency, reduce manual effort, and increase overall impact
Our take
Cobalt is a penetration testing platform providing PtaaS to significantly improve the efficiency and deliverability of penetration testing. The company is working to transform a multi-billion dollar industry that is set to grow in value as vulnerabilities become an increasing liability. Usefully for Cobalt, this applies across smaller and larger businesses alike, as the use (and protection) of data becomes increasingly important across all scales.
Its approach, using a curated marketplace of 400+ highly vetted ethical hackers, is pioneering in an industry typically hobbled by inefficient practices. Existing services tend to deliver a PDF list of vulnerabilities at the end of a testing period. Cobalt delivers results in real-time, integrated into the client’s existing environment.
In an age where hacker tech is continuously and rapidly developing, a service allowing companies to match this with continuous (as opposed to periodic) pen-testing will no doubt prove a vital and attractive tool.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
Led by a woman
Top investors
Company
Funding (last 2 of 4 rounds)
Aug 2020
$29m
SERIES B
May 2018
$5m
SERIES A
Total funding: $36.5m
Company benefits
- Equity plan
- 401k matching for retirement savings plan
- Paid parental leave
- Flexible PTO, to support work life balance from anywhere in the US, UK, and Germany
- Monthly WFH stipends for equipment, lunches, wellness and learning & development
- Work directly with experienced senior leaders
- Make your mark in the rapidly expanding PtaaS space
- Join an award-winning “best workplace” and fast growing team
- Medical, dental and life insurance benefits
Company HQ
South Beach, San Francisco, CA
Leadership
Sonali Shah
(CEO)Joined Cobalt as CEO in 2024. They are a seasoned Business Leader with more than 20 years of experience scaling high-growth businesses across the cybersecurity landscape.
Lisa Matherly
(CMO)Joined Cobalt as CMO in 2023. They have over 2 decades of experience in B2B Marketing and Cyber Security. Prior to Cobalt, they spent most of their career at McAfee, Intel, and 3Com in various leadership roles across Marketing, Customer Service, and Sales.
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