job requirements
Programming Skills:
Proficiency in Python (highly preferred) or R.
Strong command over programming paradigms required for data manipulation and modeling.
Database & Data Management:
Solid understanding of SQL and experience with both relational and non-relational databases.
Proven ability to handle large-scale data environments.
Machine Learning & Statistical Modeling:
Strong background in machine learning principles, including supervised and unsupervised learning techniques.
Competence in deep learning frameworks and statistical modeling.
Communication & Collaboration:
Ability to clearly communicate complex technical concepts to both technical and non-technical stakeholders.
Proven experience working in collaborative, cross-functional teams.
Specific Skills
Time Series Analysis:
Experience with time series forecasting and analysis techniques, including trend analysis, seasonality, and anomaly detection.
Artificial Intelligence & Machine Learning:
Expertise in AI-driven methods and algorithms such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
Experience in applying advanced deep learning techniques.
CI/CD for Data Science:
Familiarity with Continuous Integration and Continuous Deployment (CI/CD) practices for streamlining model deployment and
ensuring robust, reproducible pipelines.
PyTorch Proficiency:
Hands-on experience with PyTorch for building, training, and fine-tuning deep learning models.
Understanding of model optimization and GPU-based computations.
Data Engineering & Feature Engineering:
Skills in collecting, cleaning, and structuring data.
Experience in feature engineering to enhance model performance.
Model Development & Monitoring:
Ability to design, develop, deploy, and monitor predictive models in production environments.
Responsibilities
Project Management & Strategy:
Identify, formulate, and manage data-driven projects aligned with business objectives.
Propose innovative solutions and new approaches based on emerging trends.
Cross-Functional Collaboration:
Work closely with product managers, software engineers, and business analysts to integrate data science solutions.
Facilitate technical discussions and translate data insights into actionable business strategies.
Continuous Improvement & Innovation:
Stay current with the latest advancements in data science, AI, and related technologies.
Experiment with new tools and techniques to continually improve modeling strategies.
Preferred Qualifications
Advanced Academic Credentials:
A Master’s or Ph.D. in a relevant field is advantageous.
Domain-Specific Experience:
Experience in finance or other industry-specific applications is considered a plus.
Additional knowledge in image processing and Natural Language Processing (NLP) can be beneficial.
Big Data & Stream Processing:
Familiarity with big data tools and platforms such as Spark, Hive, Kafka, etc., is desirable.
Team Leadership:
Lead initiatives that drive the adoption of best practices within the team.
Work Environment & Soft Skills
Teamwork & Adaptability:
Ability to thrive in a dynamic, collaborative work environment.
Flexibility in handling new challenges and adjusting to evolving project requirements.
Ethical Standards & Best Practices:
Commitment to maintaining high ethical standards in data handling and model development.
Ensuring compliance with data privacy and security protocols.
Effective Communication:
Strong written and verbal communication skills, essential for stakeholder management and cross-departmental collaboration