<p style="margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><b><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Data Scientist</span></span></b></span></span></span></p> <p style="margin-bottom:11px"><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:Aptos">SWA Dallas office, TX (onsite)</span></span></p> <p style="margin-bottom:11px"><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:Aptos">Rate Only W2</span></span></p> <p style="margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><b><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Job Description: Data Scientist</span></span></b></span></span></span></p> <p><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">We are seeking a skilled and motivated Data Science Supervisor to lead a team of data scientists and analysts in delivering impactful insights and data-driven solutions.
The ideal candidate will have a strong background in data science, machine learning, and statistical analysis, paired with excellent leadership and project management skills.
This role requires the ability to manage projects, mentor team members, and collaborate effectively with cross-functional teams to drive business decisions.
</span></span></p> <p><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Additionally, we are looking for a talented Data Scientist with expertise in unsupervised learning techniques to extract meaningful patterns, insights, and structures from unlabeled data, as well as a Data Scientist with expertise in Reinforcement Learning (RL) to develop, implement, and optimize RL algorithms for complex real-world problems.
Both roles require a strong foundation in statistical analysis, machine learning, data visualization, and collaboration with cross-functional teams to create intelligent systems that learn and adapt, driving innovative solutions and data-driven decision-making.</span></span></p> <p style="margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><b><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Aptos",sans-serif"><span style="color:black">Required Technical Skills</span></span></span></b><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Aptos",sans-serif"><span style="color:black">:</span></span></span></span></span></span></p> <ul style="list-style-type:circle"> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Widely used for ML, with libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, and NumPy.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Statistical analysis and data visualization.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">SQL For querying and managing relational databases.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Machine Learning Frameworks and Libraries</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Tensor Flow and Keras: For deep learning model development.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">PyTorch: Popular for research and production in DL.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Scikit-learn: Essential for traditional ML algorithms.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">For boosting models.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Hugging Face Transformers: For NLP tasks.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Pandas: Data manipulation and analysis.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">NumPy: Numerical computing.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Apache Spark and Hadoop: For big data processing.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">ETL Tools: To handle data pipelines.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Knowledge of data wrangling, cleaning, and pre-processing.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Decision Trees: Tree-like structures used for classification and regression tasks.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Random Forests: An ensemble of decision trees that improves accuracy and reduces overfitting.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Gradient Boosted Trees: Combines weak learners (trees) sequentially to minimize loss (e.g., XGBoost, LightGBM, CatBoost).</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">K-Means Clustering: Divides data into </span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">k clusters by minimizing intra-cluster variance.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Hierarchical Clustering: Builds a tree of clusters (dendrogram) based on the hierarchy.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Groups points based on density, identifying noise and outliers.</span></span></li> <li style="margin-left:32px"><span style="font-size:12pt"><span style="font-family:"Times New Roman",serif">Gaussian Mixture Models (GMMs): Uses probabilistic models assuming data comes from a mixture of Gaussian distributions.</span></span></li>
</ul> <p style="margin-left:72px; margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><b><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Aptos",sans-serif"><span style="color:black">Soft Skills</span></span></span></b><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Aptos",sans-serif"><span style="color:black">:</span></span></span></span></span></span></p> <ul style="margin-bottom: 11px; "> <li style="list-style-type:none"> <ul style="list-style-type:circle"> <li style="margin-left:40px; margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Aptos",sans-serif"><span style="color:black">Excellent problem-solving and analytical skills.</span></span></span></span></span></span></li> <li style="margin-left:40px; margin-bottom:11px"><span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-IN" style="font-size:12.0pt"><span style="font-family:"Aptos",sans-serif"><span style="color:black">Strong communication and collaboration abilities.</span></span></span></span></span></span></li> </ul> </li>
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<span lang="EN-IN" style="font-size:12.0pt"><span style="line-height:107%"><span style="font-family:"Aptos",sans-serif"><span style="color:black">Ability to work in a fast-paced, dynamic environment</span></span></span></span>