Data Scientist

Remote

Key Responsibilities

Design, develop, and evaluate cutting-edge machine learning models to solve complex business problems, improving predictive accuracy and scalability.
Perform advanced data wrangling, transformation, and joining techniques to integrate datasets from multiple sources, ensuring data consistency, completeness, and quality.
Lead feature engineering efforts, leveraging domain knowledge and statistical techniques to extract, select, and construct the most informative features for model performance.
Collaborate closely with data engineers to enhance data infrastructure and enable efficient access to structured and unstructured data sources.
Optimize model performance through rigorous hyperparameter tuning, A/B testing, and continuous model monitoring and evaluation.
Communicate complex model insights and recommendations to stakeholders through clear, compelling visualizations and business-focused reporting.
Mentor junior data scientists and provide guidance on best practices in machine learning and data engineering.

Required Skills and Qualifications

3 to 5 years of experience as a Data Scientist or Machine Learning Engineer, with a strong track record of delivering high-impact projects.
Proficient in Python or R, with expertise in machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch.
Proven competence in SQL and experience with joining, cleaning, and transforming data from diverse sources.
In-depth understanding of feature engineering and its impact on model accuracy, interpretability, and scalability.
Hands-on experience with supervised and unsupervised machine learning techniques, including regression, classification, clustering, and neural networks.
Familiarity with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn) to effectively communicate insights.
Strong problem-solving skills, attention to detail, and ability to work collaboratively in a dynamic environment.

Preferred Experience

Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, GCP).
Familiarity with MLOps principles and deploying machine learning models into production environments.
Background in domain-specific industries (e.g., finance, healthcare, e-commerce) and understanding of relevant business challenges.
Excellent communication and interpersonal skills, with the ability to translate technical concepts to non-technical stakeholders.