This position is available as a hybrid or remote work schedule. Essential Duties, Responsibilities ... Design, build and implement machine learning models, including the development of AI Models and ...
This position is available as a hybrid or remote work schedule. Essential Duties, Responsibilities ... Design, build and implement machine learning models, including the development of AI Models and ...
Remote Computational Modeling information
What are the key skills and qualifications needed to thrive as a Remote Computational Modeling Specialist, and why are they important?
To excel in Remote Computational Modeling, you need a strong background in mathematics, physics, and computer science, often supported by a relevant degree such as in engineering or applied sciences. Proficiency with modeling software (like MATLAB, ANSYS, or COMSOL), programming languages (such as Python or C++), and cloud computing platforms is typically required. Outstanding analytical thinking, problem-solving abilities, and effective remote communication skills set top candidates apart. These competencies ensure accurate model development, efficient collaboration, and the ability to deliver reliable results in a remote work environment.
What is remote computational modeling?
Remote computational modeling is the process of creating and simulating mathematical models of real-world systems using computer software, performed from a location outside a traditional office or laboratory setting. Professionals in this field use specialized software to analyze complex data, make predictions, and solve scientific or engineering problems, all while collaborating virtually with teams or clients. This remote setup allows for greater flexibility and access to global projects, making it an attractive option for computational scientists, engineers, and analysts.
What is the difference between Remote Computational Modeling vs Remote Data Analysis?
| Aspect | Remote Computational Modeling | Remote Data Analysis |
|---|---|---|
| Required Credentials | Degree in computational science, engineering, or related fields; programming skills | Degree in statistics, data science, or related fields; analytical skills |
| Work Environment | Collaborative teams, research labs, or industry projects involving simulations | Data-focused environments, business analytics, or research settings |
| Industry Usage | Engineering, scientific research, product development | Business, marketing, healthcare, finance |
| Search & Comparison Intent | Understanding roles involving simulation and modeling techniques | Analyzing data sets to derive insights |
Remote Computational Modeling involves creating simulations and models to predict or analyze complex systems, often requiring programming and scientific expertise. Remote Data Analysis focuses on examining data sets to extract meaningful insights, typically using statistical tools. While both roles require analytical skills and often overlap in technical knowledge, they serve different purposes within industries like engineering, research, and business.
What are some common challenges faced by professionals in remote computational modeling roles, and how can they be addressed?
Professionals in remote computational modeling often face challenges such as maintaining effective communication with team members, managing complex simulations across distributed systems, and staying aligned with project goals without in-person oversight. To overcome these obstacles, it's important to leverage collaboration tools, establish regular check-ins with your team, and document your work thoroughly. Additionally, setting up a reliable remote work environment with necessary software and high-speed internet can help ensure productivity and minimize technical disruptions.
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Full-time
Posted 5 days ago
Job description
Wilson Sonsini is the premier legal advisor to technology, life sciences, and other growth enterprises worldwide. We represent companies at every stage of development, from entrepreneurial start-ups to multibillion-dollar global corporations, as well as the venture firms, private equity firms, and investment banks that finance and advise them. The firm has approximately 1,100 attorneys in 17 offices: 13 in the U.S., two in China, and two in Europe. Our broad spectrum of practices and entrepreneurial spirit allow exceptional opportunities for professional achievement and career growth.
The Innovation and AI Solutions Engineer position is part of the firm's Innovation Department.This position will be responsible for developing, optimizing and growing the firm's corpus of innovation and AI solutions for both practice and enterprise side use cases, as well as optimizing data collection and flows across these solutions.The role will support our attorneys and staff with software development, low/no-code solutions, and data initiatives. The Innovation and AI Solutions Engineer is a self-directed, people-oriented employee who is comfortable supporting the development and data needs of the organization, including clients, attorneys, practice groups and administrative teams.
This position is available as a hybrid or remote work schedule.
Essential Duties, Responsibilities
Support innovation projects by facilitating and participating in discussions, meetings with management and department groups, developing business processes, implementing data integrations and recommending best practices on the effective use of data and data analytics tools.
Build and maintain innovation and AI solutions alongside optimal data API and pipeline architectures.
Design, build and implement machine learning models, including the development of AI Models and prompts for various applications.
Collaborate with business users and technical teams to support and improve how data is collected, analyzed and reported throughout the organization.
Analyze users needs to determine business and data requirements, effectively applying technology to meet the firm's strategic objectives.
Assemble and manage large, complex data sets that meet business and technical requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, and improving infrastructure within assigned projects and data systems.
Build system infrastructure and processes for the efficient extraction, transformation, loading and integration of data to and from a wide variety of data sources using SQL, Power BI and Azure technologies.
Develop and/or apply analytics tools that utilize data pipelines to provide actionable insights into customer acquisition, operational efficiency, billing analytics, case outcomes, and other key business and legal practice performance metrics.
Ensure firm and client data security across multiple office locations, data centers, vendors and applications.
Develop data visualization and reporting tools to effectively convey meaningful insights from complex and diverse data sets.
Leverage statistics and computational techniques in problem solving.
Be familiar with common NLP and machine learning techniques.
Research and use applicable models in creative problem solving.
Work with GitHub and software version control.
Demonstrate the ability to efficiently and smartly document code and end-to-end processes.
Implement models in a production environment via custom API's and enterprise solutions. such as Azure.
Experience, Knowledge and Abilities
Extensive experience and working knowledge with SQL and relational databases, including query authoring, database management, and creating/maintaining large data stores in SQL and cloud platforms such as Azure or AWS.
Experience building and optimizing API's and data pipelines, architectures and data sets.
Strong analytic skills related to working with structured and unstructured datasets.
Ability to learn new tools and coding languages where required.
Ability to organize tasks and priorities under minimal supervision.
Experience performing root cause analysis on internal and external data, data integrations and processes to solve specific business problems and identify opportunities for improvement.
Experience developing processes that support data transformation, integrations, data structures, metadata, dependency and workflow management.
A successful history of manipulating, processing and extracting value from large, disconnected datasets.
Experience collaborating with cross-functional teams and stakeholders in a dynamic environment.
Ability to handle sensitive and confidential information responsibly.
Experience with AI/ML data preparation including feature engineering, data preprocessing, and dataset versioning for machine learning workflows.
Knowledge of data bias detection and mitigation techniques to ensure AI models are fair and representative across different legal contexts.
Experience with vector databases and embeddings for semantic search and retrieval-augmented generation (RAG) applications.
Technological Proficiency
Expertise in applying the following technologies:
SQL Server, T-SQL, SSIS & SSRS, Stored Procedures
DataWarehousing experience - ETL & ELT
PowerBI, Data Analysis Expressions (DAX)
Excel / PowerQuery
Programming and scripting languages: Python, R, C++, Julia, Javascript, SQL
API integration and development tools and scripting
Extensive experience working with a variety of data file formats, such as JSON, XML, SQL
Additional skills that would be highly advantageous include:
PowerShell
Regular Expression (Regex)
VBA, MS Access & Excel
Documentation & Process Mapping
Dynamic visualization tools, such as Microsoft Power BI, Tableau, Domo, etc.
Experience developing and applying machine learning models using Python, R, SQL and Azure Machine Learning
Experience integrating legal industry, line-of-business applications, such as SharePoint, Aderant Financial System, Salesforce.com/CRM, NetDocs/DMS
Large Language Model Integration: Experience with OpenAI API, Azure OpenAI, Anthropic Claude, or similar
Embedding Models: Familiarity with sentence transformers, OpenAI embeddings, or domain-specific legal embeddings
Law firm experience a plus.
Requirements
4+ years of experience in a Data Engineer/DataOps-DevOps role.
Bachelor's degree and/or graduate degree in Computer Science, Data Science, Data Analytics, Information Systems or equivalent discipline.
Experience with Microsoft SQL Server and related Microsoft data management and integration technologies.
Excellent verbal and written communication and interpersonal skills
Preferred
AI/ML Data Preparation Certification or equivalent coursework in machine learning data engineering.
Experience with legal technology platforms and understanding of legal workflow requirements.
Knowledge of data privacy regulations (GDPR, CCPA, HIPAA) as they apply to AI systems in legal contexts.
The compensation for this position may include a discretionary year-end merit bonus based on performance. We offer a highly competitive salary and benefits package.
Benefits information can be found here. Equal Opportunity Employer (EOE).