EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

Executive Summary

Kostić Stefan is a well-credentialed Principal Data Scientist with a PhD, 11 years of progressive experience, and a track record spanning telecom, banking, and technology sectors. their academic rigor and churn/prediction modeling background translate well to the quantitative modeling demands of this AI recruiting platform role. However, three required skills — A/B testing, causal inference, and PyTorch/TensorFlow — are absent or unconfirmed in their materials, and the lack of LinkedIn, GitHub, and a cover letter reduces transparency. This candidate is assessed as a FIT candidate contingent on interview validation of these gaps. If confirmed, their seniority and research depth could make him a strong contributor within the data science team.

Top Strengths

✓PhD-level expertise in unsupervised ML and predictive modeling with directly applicable telecom and banking domain experience
✓Demonstrated career growth to Principal Data Scientist with cross-industry versatility across telecom, banking, IoT, and smart home tech
✓Published researcher with peer-reviewed papers (including MDPI Entropy, IF: 2.494) and a granted US patent — strong intellectual credibility
✓Exceptional academic performance (GPA equivalent top marks at all three degree levels) indicating strong quantitative fundamentals
✓Broad programming proficiency across Python, SQL, R, SAS, and C++ with big data platform exposure (Spark, Hadoop)

Key Concerns

!Critical gaps in A/B testing, causal inference, and PyTorch/TensorFlow — three of six required skills are unconfirmed or missing from the resume
!Absence of LinkedIn, GitHub, and cover letter reduces confidence in candidate's engagement level and modern tooling alignment

Culture Fit

72%

Growth Potential

Moderate

Salary Estimate

$90k-$115k

Assessment Reasoning

Stefan meets the FIT threshold with an overall score of 82. they satisfies the experience level requirement comfortably (11 years total, 9 relevant), holds a PhD directly in the ML domain required, and demonstrates strong alignment on Python, SQL, statistical modeling, machine learning, and data pipeline architecture — five of eight required skills. their PhD dissertation on churn prediction using unsupervised ML, combined with banking risk analytics and telecom data science leadership, maps directly to the quantitative intelligence problems the platform is solving. The primary risk factors are the unconfirmed skills in A/B testing, causal inference, and deep learning frameworks, which must be validated through technical screening. The absence of a public professional profile (LinkedIn/GitHub) mildly reduces confidence but does not negate the strong resume signal. Salary expectations are estimated to fall within the posted range. A structured technical interview with focus on experimentation design and modern ML tooling is strongly recommended before advancing.

Interview Focus Areas

A/B testing and experimentation design: Has the candidate designed or analyzed controlled experiments in product or operational contexts?Causal inference methodology: Familiarity with methods such as DiD, propensity score matching, or instrumental variablesModern Python ML stack: Depth of PyTorch/TensorFlow usage and production ML pipeline experienceData-driven decision making examples: Specific cases where quantitative models directly influenced business outcomesRemote work and collaboration style: How does the candidate operate in distributed, asynchronous team environments?

Code Review

FairSenior Level

No code samples or GitHub profile were provided, making direct code quality assessment impossible. The candidate's tooling references suggest familiarity with Python but a primary background in SAS-centric workflows, which may indicate a gap relative to modern Python-first ML engineering standards. This area warrants a technical screening exercise.

PythonSQLSASRC++MATLABApache SparkApache Hadoop

+Breadth of programming languages suggests strong computational foundation
+Academic publications imply reproducible, methodologically rigorous analytical work
+US patent indicates applied engineering output beyond theoretical research

-No GitHub profile provided — unable to assess code quality, style, or open-source contributions directly
-Technical stack leans heavily on SAS and legacy tools rather than modern Python ML ecosystem
-No visibility into production ML code, pipeline architecture, or software engineering practices

Experience Overview

11y total · 9y relevant

This candidate is a highly credentialed Principal Data Scientist with a PhD, 11 years of experience, and a strong publication record. their background in churn prediction, clustering, and applied ML maps well to the quantitative modeling requirements of this role. Key gaps exist around A/B testing, causal inference, and modern deep learning frameworks, which would need to be assessed in interviews.

Matching Skills

PythonSQLStatistical ModelingMachine LearningData Pipeline Architecture

Skills to Verify

A/B TestingCausal InferencePyTorch / TensorFlow

Candidate information is anonymized. Personal details are hidden for fair evaluation.

Quantitative Data Scientist