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For Companies At a Glance
- Tasks: Conduct data analysis and machine learning independently while collaborating with a dynamic team.
- Company: FactSet is a global leader in financial data and analytics, empowering finance professionals worldwide.
- Benefits: Enjoy remote work options, tech certification reimbursements, and vibrant learning communities.
- Why this job: Join a cutting-edge team that impacts product development and drives informed business decisions.
- Qualifications: 3+ years of industry experience in data processing and machine learning; proficiency in Python and cloud computing.
- Other info: Opportunities for continuous learning and professional growth through various internal programs.
The predicted salary is between 43200 - 72000 £ per year.
About FactSet: FactSet is a global leader in providing cutting-edge research and analytical tools to finance professionals. We offer instant access to accurate and comprehensive financial data and analytics worldwide. FactSet clients integrate hundreds of databases from industry-leading suppliers into a single, powerful information system.
About Enterprise Analytics: The Enterprise Analytics group at FactSet focuses on internal information to support product development and internal sales teams. Our work spans a diverse array of projects and teams, reflecting our broad scope and impact. We analyze user engagement patterns to identify trends and at-risk users, and recommend product bundling strategies. Our team processes and examines internal documents to uncover opportunities, and we are at the forefront of developing tools to work with data from LLM-powered tools. We collaborate closely with our stakeholders over extended periods, helping the business make informed, impactful decisions. Throughout these processes, we leverage both traditional and state-of-the-art machine learning and data analytics techniques, ensuring we remain at the cutting edge of the industry.
Job Responsibilities:
- Manage and conduct data analysis and machine learning methodologies independently. This could involve running experiments, creating models, and interpreting results.
- Access data from various sources and prepare it for analysis.
- Handle cleaning of complex datasets by identifying, addressing, and resolving issues related to quality and integrity.
- Create and manage git repositories efficiently.
- Write clean, efficient, and reusable code adhering to best practices.
- Proficiency in unit testing, code profiling and cloud computing.
- Work collaboratively with the data science team and other stakeholders.
- Communicate effectively about complex tasks, projects and insights generated from data.
- Present findings in a comprehensible manner to both technical and non-technical audiences.
Technology Learning Opportunities:
FactSet is committed to invest into Career development of all the Engineers to upskill, or re-skill based on individual interests, Project priorities and offers:
- Licenses for learning resources like Pluralsight
- Reimbursement of Technology Certification Fees (Azure, AWS or relevant Technologies)
- Paid Leave for Certification Exam preparation (In addition to Casual Leaves and Privilege Leaves)
- Vibrant Technology Communities that organize Internal programs, technology symposiums, Guest lectures by internal and external experts.
Requirements:
- We are seeking a results-oriented person with at least three years of experience full-time Industry work in understanding of machine learning techniques and data processing.
- Proficiency in relevant programming languages (e.g. Python).
- Ability to effectively manage git repositories and experience with cloud computing platforms.
- Expertise in accessing, cleaning, processing, and handling complex data for analysis.
- Excellent problem-solving skills and ability to design and execute advanced experiments testing hypotheses.
- Strong communication skills for effectively presenting findings to stakeholders and closely collaborating with team members.
- Experience with unit testing, code profiling, and object-oriented programming.
- Ability to work on multiple projects simultaneously and adapt to dynamic work environments.
- Experience with Big Data platforms like Hadoop or Spark and knowledge of SQL is a plus.
- Proficiency with statistical programming and data visualization tools is highly desirable.
- Continual learning attitude, with a focus on enhancing both technical and soft skills.
Data Scientist II employer: FactSet Research Systems, Inc.
Contact Detail:
FactSet Research Systems, Inc. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist II
✨Tip Number 1
Familiarise yourself with the specific machine learning techniques and data processing methods mentioned in the job description. Being able to discuss these in detail during your interview will show that you have a solid understanding of the role and its requirements.
✨Tip Number 2
Brush up on your skills with git repositories and cloud computing platforms. Consider working on personal projects or contributing to open-source projects to demonstrate your proficiency, as this practical experience can set you apart from other candidates.
✨Tip Number 3
Prepare to discuss your problem-solving approach and how you've handled complex datasets in the past. Be ready to share specific examples of challenges you've faced and how you overcame them, as this will highlight your analytical skills and adaptability.
✨Tip Number 4
Practice presenting your findings in a clear and engaging manner. Since effective communication is key for this role, consider doing mock presentations to friends or colleagues to refine your ability to convey complex information to both technical and non-technical audiences.
We think you need these skills to ace Data Scientist II
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data analysis, machine learning, and programming languages like Python. Emphasise any projects where you've managed git repositories or worked with cloud computing.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role at FactSet and how your skills align with their needs. Mention specific experiences that demonstrate your problem-solving abilities and communication skills.
Showcase Your Projects: If you have any personal or professional projects related to data science, include them in your application. Highlight your role, the technologies used, and the outcomes achieved to illustrate your hands-on experience.
Prepare for Technical Questions: Anticipate technical questions related to machine learning techniques, data processing, and coding practices. Be ready to discuss your approach to cleaning complex datasets and managing experiments during interviews.
How to prepare for a job interview at FactSet Research Systems, Inc.
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning techniques and data processing. Highlight specific projects where you've applied these skills, and be ready to explain your approach to cleaning and analysing complex datasets.
✨Demonstrate Collaboration
Since the role involves working closely with stakeholders and team members, share examples of how you've successfully collaborated in past projects. Emphasise your communication skills and how you present findings to both technical and non-technical audiences.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving abilities. Think of scenarios where you've designed and executed experiments or tackled complex issues. Be ready to walk through your thought process and the outcomes of your solutions.
✨Familiarise Yourself with Git and Cloud Computing
As managing git repositories and cloud computing is crucial for this role, ensure you can discuss your proficiency in these areas. Consider preparing a brief demonstration of how you've used these tools effectively in your previous work.
