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Data Scientist Job Opening In Dallas – Now Hiring Tek Leaders Inc


Job description

<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>
</ul>
<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>

Required Skill Profession

Mathematical Science Occupations


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Unlock Your Data Scientist Potential: Insight & Career Growth Guide


Real-time Data Scientist Jobs Trends (Graphical Representation)

Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for Data Scientist in Dallas, United States, highlighting market share and opportunities for professionals in Data Scientist roles.

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554 Jobs in Dallas
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Are You Looking for Data Scientist Job?

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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at Tek Leaders Inc adheres to the cultural norms as outlined by Expertini.

The fundamental ethical values are:

1. Independence

2. Loyalty

3. Impartiapty

4. Integrity

5. Accountabipty

6. Respect for human rights

7. Obeying United States laws and regulations

What Is the Average Salary Range for Data Scientist Positions?

The average salary range for a varies, but the pay scale is rated "Standard" in Dallas. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.

What Are the Key Qualifications for Data Scientist?

Key qualifications for Data Scientist typically include Mathematical Science Occupations and a list of qualifications and expertise as mentioned in the job specification. The generic skills are mostly outlined by the . Be sure to check the specific job listing for detailed requirements and qualifications.

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To improve your chances of getting hired for Data Scientist, consider enhancing your skills. Check your CV/Résumé Score with our free Tool. We have an in-built Resume Scoring tool that gives you the matching score for each job based on your CV/Résumé once it is uploaded. This can help you align your CV/Résumé according to the job requirements and enhance your skills if needed.

Interview Tips for Data Scientist Job Success

Tek Leaders Inc interview tips for Data Scientist

Here are some tips to help you prepare for and ace your Data Scientist job interview:

Before the Interview:

Research: Learn about the Tek Leaders Inc's mission, values, products, and the specific job requirements and get further information about

Other Openings

Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

Dress Professionally: Choose attire appropriate for the company culture.

Prepare Questions: Show your interest by having thoughtful questions for the interviewer.

Plan Your Commute: Allow ample time to arrive on time and avoid feeling rushed.

During the Interview:

Be Punctual: Arrive on time to demonstrate professionalism and respect.

Make a Great First Impression: Greet the interviewer with a handshake, smile, and eye contact.

Confidence and Enthusiasm: Project a positive attitude and show your genuine interest in the opportunity.

Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

Ask Prepared Questions: Demonstrate curiosity and engagement with the role and company.

Follow Up: Send a thank-you email to the interviewer within 24 hours.

Additional Tips:

Be Yourself: Let your personality shine through while maintaining professionalism.

Be Honest: Don't exaggerate your skills or experience.

Be Positive: Focus on your strengths and accomplishments.

Body Language: Maintain good posture, avoid fidgeting, and make eye contact.

Turn Off Phone: Avoid distractions during the interview.

Final Thought:

To prepare for your Data Scientist interview at Tek Leaders Inc, research the company, understand the job requirements, and practice common interview questions.

Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the Tek Leaders Inc's products or services and be prepared to discuss how you can contribute to their success.

By following these tips, you can increase your chances of making a positive impression and landing the job!

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