Job Description

Lead Data Scientist

Primary Skills

Classification (Decision Trees, SVM), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Great Expectation, Evidently AI, Hypothesis Testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Probabilistic Graph Models, Python/PySpark, R/ R Studio, Regression (Linear, Logistic), SAS/SPSS, Statistical analysis and computing, Tools(KubeFlow, BentoML), T-Test, Z-Test

Specialization

Data Science Advanced: Data Science Lead

Job requirements

Key Responsibilities
Strategic Leadership: Define the data science vision, strategy, and roadmap in alignment with business objectives. Lead and oversee the execution of complex data science projects from inception to deployment. Drive innovation and identify opportunities for leveraging data to achieve business goals.
Team Management: Manage, mentor, and develop a team of data scientists and analysts. Foster a collaborative and inclusive team environment. Provide technical guidance and support to team members.
Data Collection and Processing: Oversee the collection, cleaning, and preprocessing of large and complex datasets from various sources. Ensure data integrity, quality, and security. Develop and maintain scalable data pipelines and infrastructure.
Exploratory Data Analysis (EDA): Lead in-depth EDA to uncover insights, trends, and patterns in data. o Visualize data using advanced tools and techniques to communicate findings effectively.
Model Development and Evaluation: Design, develop, and implement advanced machine learning models and algorithms. Perform rigorous model evaluation and validation using appropriate metrics and techniques. Optimize models for performance, accuracy, and scalability.
Model Deployment and Integration: Oversee the deployment of machine learning models into production environments. Ensure seamless integration with existing systems and applications. Monitor and maintain models in production, ensuring continued performance and accuracy.
Collaboration and Communication: Collaborate with cross-functional teams, including product managers, engineers, and business stakeholders, to understand requirements and deliver solutions. Communicate complex technical concepts and results to non-technical stakeholders. Translate business needs into data science problems and solutions.
Research and Innovation: Stay abreast of the latest advancements in data science, machine learning, and AI. Experiment with new tools, techniques, and methodologies to enhance existing processes and models. Contribute to the data science community through publications, presentations, and open-source contributions. Qualifications
Education: Master’s or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related field.
Experience: At least 7 years of experience in data science, with a proven track record of leading and delivering successful data science projects. Experience in managing and mentoring data science teams.
Technical Skills: Expertise in programming languages such as Python, R, or Java. Strong knowledge of machine learning frameworks and libraries, including TensorFlow, PyTorch, scikit-learn, and Keras. Proficiency with data processing tools like Pandas, NumPy, and SQL. Familiarity with big data technologies such as Hadoop, Spark, and distributed computing. Experience with cloud platforms like AWS, Google Cloud, or Azure.
Analytical Skills: Exceptional problem-solving skills and the ability to analyze complex datasets.
Communication Skills: Excellent verbal and written communication skills, with the ability to convey technical concepts to non-technical stakeholders.
Leadership Skills: Demonstrated ability to lead projects and teams, and to drive results in a collaborative environment.