Data Scientist

OUR HIRING PROCESS:

• We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment.
• We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team.
• At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team.
• From there on, it's decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live. After all, we consider our team members our family, and we want you to feel comfortable and welcomed.
  
  

We are seeking a highly skilled and motivated Data Scientist to join our dynamic team. The ideal candidate will have a strong background in data analysis, machine learning, and statistical modeling, with a keen ability to translate data insights into actionable business strategies.

Key Responsibilities:

• Develop and implement AI/ML models and algorithms, particularly focusing on LLM.
• Managing the entire data lifecycle, from data collection and cleaning to model training, deployment, and inference.
• Be proficient in utilizing various data engineering tools and frameworks to ensure seamless data flow and possess the ability to build, train, and deploy machine learning models in production environments.
• Analyze complex datasets using strong analytical skills to identify trends, patterns, and actionable insights.
• Conduct a thorough evaluation of AI and machine learning tools and strategically adopt those that have the potential to improve key business processes significantly.
• Collaborate on defining and implementing KPIs to measure business success based on insights derived from machine learning models and data analysis.
• Ensure data quality by cleaning and validating results data for accuracy.
• Analyze current data structures, tagging, and quality standards. Recommend improvements and identify and address data anomalies.
  
  

Qualifications:

• Master's degree in statistics, mathematics, economics, financial engineering, or a similar field.
• Minimum of 3 years of proven experience as a Data Analyst or Business Data Analyst.
• E2E data flow, model training/deployment and inference expertise
• Expertise in developing and deploying AI/ML models using Python libraries, including pre-processing, analysis, model selection, and performance evaluation.
• Demonstrated experience with Generative AI, feature repository development for AI/ML models, LLM embeddings and fine-tuningon custom datasets for specific use cases.
• Strong analytical skills for data manipulation, cleaning, and insightful reporting with meticulous attention to detail and accuracy.
• Strong proficiency in SQL and experience working with relational databases.
• Experience in business analysis and design, data modeling (including planning, requirement gathering, and documentation).
• Technical expertise in data models, database design development, data mining, and segmentation techniques.
• Excellent problem-solving skills with the ability to think critically and provide innovative solutions.
• Exceptional communication skills to effectively present findings and recommendations.
  
  

We offer:

• Competitive compensation, which will take into account the experience and skills of the candidate. Form of payment - monthly, based on Invoice - Bank transfer/Payoneer
• Remote, Work schedule: 5/2 (8-hour working day) from 13:00 to 22:00 EEST (incl. 60-min breaks, Flexible schedule is possible)
• Interesting and long-term project
• We support work-life balance and offer paid annual leave and paid sick days
• Shorter Fridays (6 working hours) in Summertime.
• Opportunities for professional development and career growth
  
  

Our recruitment process:

• Interview with HR + one-way video interview
• Test tasks
• Technical interview with Team
• Final interview with the Technical director