Data Scientist with AI

ScienceSoft is looking for a Senior\Lead Data Scientist with expertise in Artificial Intelligence (AI),Computer Vision, and Deep Learning to lead innovative data science initiatives in healthcare and finance.

The role involves designing and implementing advanced algorithms, analyzing complex datasets, build predictive models, and deriving actionable insights to support decision-making processes.

The Senior Data Scientist will be responsible for developing and deploying AI/ML solutions that leverage Computer Vision techniques to solve complex problems in healthcare and finance. This role requires a blend of technical expertise, analytical thinking, and domain knowledge to design and execute data-driven strategies that drive business growth and improve operational efficiency.

Your Skills and Experience:

Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field. Master’s degree is highly preferred.

5+ years of experience in data science, machine learning, CV and AI or a related field.

Proven track record in developing and deploying AI/ML models, particularly those involving Computer Vision techniques.

Strong analytical thinking skills to interpret data and derive actionable insights.

Ability to translate complex data science concepts into simple, understandable results for non-technical stakeholders.

Proficiency in statistical analysis, hypothesis testing, and predictive modeling.

Experience working with healthcare or finance data, including domain-specific challenges.

Your Responsibilities:

Develop and implement AI/ML models, particularly those involving Computer Vision (e.g., image recognition, medical imaging analysis, fraud detection, facial recognition, and automated surveillance systems).

Develop and deploy AI/ML models for applications such as disease diagnosis, patient outcome prediction, fraud detection, and risk modeling.

Develop expertise in Large Language Models (LLMs), Natural Language Processing (NLP), text and speech recognition systems, and cloud platforms such as Azure and AWS.

Lead end-to-end machine learning projects from data collection to deployment, ensuring scalability and performance.

Continuously monitor and optimize models to improve accuracy, reduce bias, and ensure ethical compliance.

Collaborate with domain experts (e.g., healthcare professionals, financial analysts) to understand requirements and define project scopes

Technical Expertise:

Strong understanding of programming languages and frame works such as Python, R, SQL, Spark, Scala.

Expertise in machine learning frameworks for like TensorFlow, PyTorch, Scikit-learn, Keras, ONNX, fast.ai and other leading frameworks.

Expertise in LLMs for healthcare text analysis (e.g., analyzing medical records or patient notes).

Proficiency in NLP techniques such as sentiment analysis, named entity recognition, and text summarization.

Knowledge of deep learning libraries (e.g., Keras, ONNX).

Familiarity with Computer Vision techniques, including convolutional neural networks (CNNs), transfer learning, and object detection.

Familiarity with cloud platforms such as AWS, Azure, GCP, and their respective data and AI tools.

Utilizing AWS SageMaker, Google AI Platform, or Azure AI for model development and deployment.

Experience in Computer Vision techniques to solve problems in healthcare (e.g., medical imaging analysis for diagnostics) and finance (e.g., fraud detection, customer behavior analysis).

Conduct data preprocessing, augmentation, and feature engineering to prepare datasets for AI/ML models.

Analyze large-scale datasets using statistical methods and visualization tools to extract meaningful insights.

Utilize advanced data engineering techniques to build robust data pipelines, ETL processes, and scalable AI solutions.

Knowledge of text-to-speech and speech-to-text technologies for healthcare applications like voice recognition systems.

Strong understanding of CNNs, RNNs, U-Nets, and other computer vision architectures applied to medical imaging, video analytics, etc.

Strong understanding of statistical analysis, hypothesis testing, and predictive modeling.

Reporting & Insights

Partner with domain experts in healthcare or finance to understand challenges and translate requirements into technical specifications.

Present findings and recommendations to non-technical stakeholders, ensuring clear communication of data-driven solutions.

Document best practices, methodologies, and results for internal knowledge sharing and future reference.

Generate actionable insights from data analysis and model performance evaluation to inform strategic decisions.

Create visualizations and reports to communicate findings effectively to stakeholders across departments.

Monitor model performance over time, identify performance degradation, and retrain models as needed.

ScienceSoft Offers

Opportunities for professional self-realization.

Competitive salary.

Friendly and supportive team.

Professional trainings and certifications paid by the company.

25 days of paid vacation.

100%-paid sick leave.

Language courses with native speakers.

Sport program.

Medical insurance.

Opportunity of remote work.