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Computer Science Data Jobs in Philadelphia, PA (NOW HIRING)

Chubb

Senior Data Scientist

Philadelphia, PA · On-site

Graduate degrees in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Economics or Engineering are preferred. Location: We are open to the ...

MSD

Assoc. Dir , Data Analysis

North Wales, PA · On-site

S. or M.S. in Engineering or related field (Business, Computer Science, Engineering, Data Science, etc.) Required: * Digital and Marketing data domain knowledge in commercial space in biopharma ...

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Computer Science Data information

What are some common challenges faced by professionals working in computer science data roles, and how can they be addressed?

Professionals in computer science data roles often encounter challenges such as handling large and complex datasets, ensuring data quality, and keeping up with rapidly evolving technologies. Collaboration with cross-functional teams is essential, as data professionals frequently work with engineers, analysts, and business stakeholders to interpret data and deliver actionable insights. To address these challenges, it's important to invest in continuous learning, leverage automation tools for data cleaning, and maintain clear communication channels within the team.

What is the difference between Computer Science Data vs Data Analyst?

AspectComputer Science DataData Analyst
Required CredentialsBachelor's or higher in Computer Science, Data Science, or related fieldsBachelor's degree in Statistics, Mathematics, or related fields
Work EnvironmentSoftware development, data engineering, algorithm designData interpretation, reporting, visualization
Employer & Industry UsageTech companies, startups, research institutionsBusiness, finance, marketing, healthcare
Common Search & ComparisonOften compared for data handling and programming skillsCompared for data interpretation and business insights

Computer Science Data professionals focus on developing algorithms, managing data systems, and building software solutions. Data Analysts primarily interpret data, create reports, and provide insights for decision-making. While both roles work with data, their core responsibilities and skill sets differ significantly.

What are the key skills and qualifications needed to thrive as a Data Scientist in Computer Science, and why are they important?

To thrive as a Data Scientist in Computer Science, you need strong analytical skills, proficiency in statistics, and a solid background in programming (often with a degree in computer science, mathematics, or a related field). Familiarity with tools like Python, R, SQL, and machine learning frameworks, as well as certifications such as AWS Certified Data Analytics or Google Data Engineer, are highly valuable. Excellent problem-solving abilities, communication skills, and curiosity help you interpret data insights and explain findings to non-technical stakeholders. These skills ensure you can extract actionable insights from complex data, drive business decisions, and collaborate effectively within multidisciplinary teams.

What is computer science data?

Computer science data refers to the information that is processed, analyzed, and utilized within the field of computer science. This data can include anything from raw numbers and text to images, audio, and video, and is often used in programming, machine learning, artificial intelligence, databases, and data analysis. Understanding how to collect, store, structure, and interpret data is a fundamental skill for computer scientists and is crucial for solving real-world problems using technology.
Computer Science Data jobs near you

Scientific - Data Scientist

Futran Tech Solutions Pvt. Ltd.

Lawrenceville, NJ • On-site

Full-time

Posted 15 days ago

Job description

Scientific - Data Scientist
Location - Lawrenceville, New Jersey 08648
Role is 100% onsite.
Junior (0-3 Yrs.)
Description
Leads discovery and optimization (LDO) is a diverse group of scientists and engineers, providing critical assay information to therapeutic research centers (TRCs) throughout research and early development (R&ED). We are seeking a highly motivated and innovative data scientist to join the data science and advanced analytics team within LDO until the end of 2023. The individual will develop a machine learning and Bayesian statistics-based approach to model assay variability using medium to high throughput screening datasets. The individual will work in a highly dynamic environment at the center of the R&ED drug discovery engine to develop cutting edge tools applied to complex drug discovery problems.
Roles and Responsibilities
• Write python scripts to enable rapid cleaning and analysis of medium and high throughput datasets
• Utilize machine learning (ML) approaches to generate small molecules features
• Utilize Bayesian statistics approaches to estimate uncertainties in assay datasets, based on results on above ML outputs
• Write and document programming code (python preferred) to facilitate data preparation / cleaning, model development, and evaluation
• Produce high quality scripts, documentation, and processing pipeline by the end of 2023
• Create deployable version of processing pipeline for near term use as a stand-alone application and ultimately future integration with enterprise suite
Qualifications
• Ph.D. in quantitative sciences/engineering (computer science, mathematics, statistics, or engineering)
• 5+ years of relevant professional experience with a proven track record in machine learning and data science - experience in drug discovery machine learning is desirable but not required
• Strong knowledge of one or more scripting programming languages, with a focus on machine learning (e.g., Python (preferred), R, Matlab, C/C++)
• Experience utilizing molecular features of small molecules in machine learning models
• Experience with the use and application of Bayesian statistics and simulation methods in generating probabilistic outcomes
• Able to extract information from databases using a variety of software packages (e.g., Oracle SQL developer)
• Ability to build and maintain databases aligned with enterprise solutions is desirable but not required
• Strong analytical and problem solving skills to understand technical business problems and implement solutions
• Ability to work effectively on matrixed teams to collaboratively solve challenging problems, while also able to work independently with minimal resources
• Has good interpersonal, communication, writing and organizational skills
• Strong preference for on-site presence to enable colocation with data science team

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