Hire the Best Data Scientists
in Armenia

I am a full-stack Python, C++ AI/ML/ Computer vision / 3d reconstruction developer ✅ Top Rated PLUS Upwork Freelancer ✅ 15000+ hours worked ✅ 120+ Jobs Completed ✅ $300k+ earned Computer Vision and Machine learning - Computer Vision | Machine learning OpenCV,PCL,ROS,Detectron,YOLO, VTK, Intel Realsense, Zed camera, Zivid camera, NLP, Transformers - Computer Vision, Image Processing, OpenCV, OpenGL, MKL, ITK, VTK - Deep Learning, caffe, Tensor flow, Pytorch - YOLO, DETECTRON -Desktop application development using C++/Qt, Python -3d reconstruction, NLP using Matlab,R, Python, OpenCV, OpenGL,CUDA,OpenCL, MKL, ITK, VTK,PCL,ROS,R, Transformers. -Machine and Deep Learning using SVM, KNN, Neural Networks(TensorFlow, Yolo, Detection, Pytorch). -GUI development, sockets. -Stereo Vision and 3d reconstruction. SLAM and SFM algorithms implementation and improvement. -Video/Audio streaming over network using LIBVLC, FFMPEG, GSTREAMER. -Natural language processing using BERT, BART. -Development of technically complex projects and scientific articles. -Generic programming, OOP. -Complex algorithms & data structures.

Most data and machine learning projects do not fail because the models are wrong. They fail because real decisions are built on systems that were never designed for production reality. Data distributions change. Assumptions stop holding. Feedback loops appear once decisions are automated. What looks correct during analysis can quietly become wrong when it starts affecting real users, money, or operations. My work focuses on that gap between analysis and real-world execution. I design and implement production decision systems, not demos, notebooks, or isolated models. These are systems where predictions directly drive actions such as approvals, rejections, prioritization, allocations, or automated workflows. In this context, correctness, transparency, and operational control matter more than theoretical performance. My background combines applied machine learning, data engineering, and direct ownership of real decision pipelines. I have worked on systems used in environments where mistakes are costly, including credit decisioning and automated trading. I approach every project assuming that data will be imperfect and conditions will change over time. Models are components of a system, not the system itself. I work system-first. That means clearly defining decision logic, data contracts, monitoring signals, and conditions for retraining or rollback. A system should surface degradation early, before it causes damage, not after. What I do not do is equally important. I do not sell AI magic, guaranteed outcomes, or opaque black-box solutions. I do not optimize offline metrics without considering how the system behaves once deployed. If a system cannot be monitored, understood, and controlled in production, I do not consider it complete. Clients usually reach out to me when decisions matter, scale is real, and failure is expensive. If you need a system that behaves predictably under pressure and you want to clearly understand both its capabilities and its limits, we are likely a good fit.

I am a Data Scientist with about 5 years of experience specializing in product analytics, A/B testing, and machine learning. Overall, I have more than 15 years of on-site work experience. I am detail-oriented, reliable, and organized, and I always make sure my work is completed accurately and on time. My expertise includes Python, SQL, and data visualization tools like Looker and Databricks. I excel at transforming complex product data into actionable insights, enabling informed decision-making that drives engagement and conversion. I have successfully designed and analyzed experiments focusing on funnel performance and user behavior across mobile and web applications. My analytical skills allow me to evaluate app features using comprehensive metrics and dashboards. If you are seeking a reliable partner to enhance your product strategy through data-driven insights, I am ready to collaborate and help elevate your results to the next level.

I am a professional mathematician with research interests in Differential Equations, Spectral Theory, Inverse Problems, Fractional Calculus, Machine Learning, Artificial Neural Networks, Statistical Natural Language Processing, and Digital Signal Processing.

I build credit risk models that help lenders approve more good borrowers, cut losses, and make underwriting decisions they can stand behind. Mostly that means credit scoring and loan default prediction models in Python — built to go into production, not prototypes that get shelved. On a recent credit scoring project, my model lifted AUC from 0.81 to 0.85. In practice that meant the lender approved 8% more applicants while the default rate went down 1% — worth roughly 10% in added profit over a one-year horizon. Before Upwork, I spent three years as an in-house Data Scientist at one of Armenia's largest banks, building forecasting and risk models used in live operations. That experience shapes how I work: I care as much about how a model holds up over time and under review as I do about its accuracy on day one. I usually work with: → Fintech and alternative/SME lenders building or improving credit decisioning → Banks and credit unions that need scorecards built, rebuilt, or validated → Risk and analytics teams that need an extra pair of expert hands What I typically deliver: → Credit scoring and application/behavioral scorecards → Credit risk modeling: PD and loan default prediction models → Model validation, monitoring, and clear documentation → Risk segmentation and lending portfolio analytics → Fraud detection models for lending → Risk dashboards and SQL/Python pipelines that replace fragile Excel reporting → IFRS 9 / ECL support when a project calls for it Tools I rely on: Python (pandas, scikit-learn, statsmodels), XGBoost, LightGBM, logistic regression, and SQL, with FastAPI and Docker when something needs to be deployed. A few honest signals: Top Rated Plus, 100% Job Success, and $50K+ earned on Upwork, including work for US-based enterprise clients. One client's feedback on a recent credit scoring project was simply "Highly recommended," and another noted my "amazing knowledge in ML and DS." How I work: a short scoping call, then a clear plan with an honest estimate, then iterative delivery with documentation your team and any reviewer can actually follow. If something isn't the right fit for me, I'll tell you. Send a short message with your problem, your data, and your timeline. I usually reply within a few hours. The first 15-minute scoping call is free.

I have PhD in Theoretical and Mathematical Physics. I am co-author or author of about 20 scientific papers. My second specialization is Data Science along with Big Data. I have also solid experience in Big Data tools like: Apache Spark, Apache Solr, Kafka, HBase, HDFS, Hive and others. I used numerous machine learning algorithms during my career like: principal component analysis, self-organized maps, k-means, k-nearest neighbours and also linear models like simple linear regression. I developed an algorithm to predict time series data using linear regression and Holt-Winters models. In my professional career in Physics (Astrophysics) I calculated tidal perturbations of Lares/Lageos satellite, constructed long range Hubble diagram for gamma-ray bursts.