Project Role : Data Science Practitioner
Project Role Description : Formulating, design and deliver AI/ML-based decision-making frameworks and models for business outcomes. Measure and justify AI/ML based solution values.
Must have skills : Data Science
Good to have skills : Machine Learning
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Job Title: Data Science Associate Manager Job Description: Summary: The Data Science Associate Manager is responsible for overseeing a team of data scientists and contributing to data-driven initiatives that address business challenges and enhance decision-making. This role requires a balance of technical expertise, leadership skills, and a deep understanding of data science methodologies. Key Responsibilities: 1. Team Leadership: • Provide leadership, guidance, and mentorship to a team of data scientists. • Foster a collaborative and innovative team environment. • Manage team performance, including goal setting, performance reviews, and career development. 2. Project Management: • Oversee the planning and execution of data science projects, ensuring they align with business objectives. • Define project scopes, timelines, and resource allocation. • Monitor project progress, identify risks, and implement mitigation strategies. 3. Technical Expertise: • Stay up-to-date with the latest data science and machine learning techniques. • Provide technical guidance and support to team members. • Act as a subject matter expert in data science, statistics, and relevant tools and technologies. 4. Data Analysis and Modeling: • Collaborate with team members to develop predictive models, machine learning algorithms, and statistical analyses. • Ensure the quality and accuracy of data analysis and modeling results. • Interpret data findings and communicate insights to non-technical stakeholders. 5. Data Strategy: • Collaborate with senior management to define the data strategy and vision for the organization. • Identify opportunities for leveraging data to drive business growth and improve operations. 6. Cross-functional Collaboration: • Work closely with other departments, such as IT, engineering, and business units, to integrate data science solutions into business processes. • Communicate effectively with stakeholders to gather requirements and provide updates on project progress. 7. Data Governance and Compliance: • Ensure data privacy and compliance with relevant regulations, such as GDPR or HIPAA. • Establish data governance best practices within the team. 8. Resource Management: • Manage the allocation of data science resources, including team members and external consultants if applicable. • Ensure the efficient utilization of resources to achieve project objectives. Technical Expectations: • Should be good in the below Algorithms 1. Supervised Learning Algorithms: • Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), Naive Bayes: 2. Unsupervised Learning Algorithms: • K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Apriori Algorithm 3. Reinforcement Learning Algorithms: • Q-Learning, Deep Q Networks (DQN), Policy Gradient Methods 4. Neural Network and Deep Learning Algorithms: • Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Transformer Models, Ensemble Learning Algorithms: Bagging, Boosting, Stacking 5. Natural Language Processing (NLP) : Text Analysis, Sentiment Analysis, Named Entity Recognition (NER), Part-of-Speech Tagging, Classification, Machine Translation, Chatbots and Virtual Assistants, Question Answering, Text Summarization, Language Generation, Speech Recognition, Language Understanding and Reasoning, Ethical and Bias Considerations 6. Standardization Techniques: Feature Scaling, Categorical Variable Encoding, Text Data Preprocessing 7. Regularization Techniques: L1 and L2 Regularization, L2 Regularization (Ridge), Elastic Net Regularization, Dropout (Neural Networks), Early Stopping, Cross-Validation, Batch Normalization, Weight Decay, Early Stopping: • A Bachelor's or Master's degree in a related field (e.g., Data Science, Statistics, Computer Science). • Proven experience in data science, machine learning, and statistical analysis. • Strong programming skills in languages such as Python or R. • Exposure to manufacturing domain is an added advantage • Exposure to cloud hyperscalar like Azure/AWS is preferred • Previous experience in a leadership or management role is preferred. • Excellent communication and presentation skills. • Strong problem-solving and analytical abilities. • Familiarity with data visualization tools (e.g., Tableau, Power BI) is a plus. • Knowledge of database technologies (SQL, NoSQL) and big data frameworks (e.g., Hadoop, Spark) is advantageous.

15 years full time education

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