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Breaking into Machine Learning and AI: A Guide to Entry-Level Roles

Artificial Intelligence (AI) and Machine Learning (ML) are among the most exciting and fast-growing fields in technology today. From recommendation engines and virtual assistants to self-driving cars and fraud detection systems, these technologies are rapidly transforming industries.

But how do you get started in this field—especially if you’re just finishing school or looking to shift into AI from another discipline? This guide will walk you through what entry-level AI/ML jobs look like, what skills they require, and how to begin building a career in this space.

What Are Entry-Level AI and ML Jobs?

Entry-level AI/ML jobs are positions that typically require a solid foundation in mathematics, statistics, and programming, but do not necessarily require years of experience or a graduate degree. These roles often include:

1. Machine Learning Engineer (Junior)

  • Builds, tests, and improves models that automate tasks or make predictions.
  • Works with data scientists and software engineers to integrate ML models into products.

2. Data Scientist (Entry-Level)

  • Cleans and analyzes data, builds statistical models, and communicates insights.
  • May use ML techniques for classification, regression, and clustering tasks.

3. AI/ML Research Assistant

  • Supports research teams with model development and literature reviews.
  • Often found in academic institutions or R&D teams in industry.

4. Data Analyst (with ML Exposure)

  • Focuses on exploratory data analysis and may use basic machine learning models for forecasting or segmentation.

5. ML Ops / AI Product Associate

  • Assists in deploying, monitoring, and maintaining ML models in production.
  • Works closely with DevOps and product teams.

What Skills Are Needed?

While requirements vary by role and company, most entry-level ML/AI jobs expect some combination of the following:

Technical Skills

  • Programming: Python (most common), R, SQL
  • Libraries/Frameworks: Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy
  • Mathematics: Linear algebra, probability, statistics, calculus
  • Machine Learning Concepts: Supervised/unsupervised learning, overfitting, model evaluation
  • Data Handling: Data cleaning, feature engineering, visualization
  • Version Control: Git/GitHub

Soft Skills

  • Problem-solving mindset
  • Communication and storytelling with data
  • Curiosity and willingness to learn

Do You Need a Master’s or PhD?

Not necessarily. While many research roles require advanced degrees, many industry positions are accessible with a bachelor’s degree in Computer Science, Data Science, Engineering, or related fields, especially if you supplement your education with hands-on projects and internships.

How to Gain Experience

If you’re just starting out, here are practical steps to build your profile:

  • Complete Online Courses or Certifications: Platforms like Coursera, edX, and Udacity offer courses from top universities and companies.
  • Work on Projects: Build ML models for real-world datasets (Kaggle is a great place to start).
  • Contribute to Open Source: Join GitHub repositories or ML-related projects to collaborate and showcase your skills.
  • Internships and Co-Ops: Many companies offer internships that involve ML and data science work—great stepping stones to full-time roles.

Companies Hiring for Entry-Level AI/ML Roles

  • Tech Giants: Google, Meta, Microsoft, Amazon, Apple
  • Startups: AI-focused startups often look for energetic generalists with practical skills
  • Finance & Healthcare: Firms like JPMorgan Chase, UnitedHealth Group, and CVS Health are investing heavily in AI talent
  • Retail and Logistics: Walmart, Target, and UPS use ML for customer experience, inventory forecasting, and logistics

The field of machine learning and AI is constantly evolving, and the demand for skilled professionals continues to rise. Entry-level roles in this space are competitive, but with the right combination of technical foundations, practical experience, and curiosity, you can carve out a rewarding career path. Start small, stay consistent, and build your skills one project at a time.

Economics and Business Analytics double major and a Data Science minor at Lawrence University as well as an Event Coordinator at Pan Asian Organization and Social Media Manager at LU Data Science Club. Connect with Nadika in LinkedIn

Leadership Development Programs and Rotational Programs

For students and early-career professionals exploring post-graduate opportunities, Leadership Development Programs (LDPs) and Rotational Programs offer a unique and structured pathway to gain experience, build skills, and grow into leadership roles.

What Is a Leadership Development or Rotational Program?

Leadership Development Programs are structured initiatives, typically offered by large organizations, designed to develop future leaders. They often span one to three years and focus on accelerating the professional growth of high-potential talent through targeted experiences, mentorship, and training.

Rotational Programs are a specific type of LDP where participants cycle through multiple departments or roles within the company. These rotations are planned to expose participants to various aspects of the business—such as marketing, operations, finance, and product development—helping them build a broad foundation of knowledge and skills.

Common Features:

  • Structured rotations across departments or business units
  • Formal learning and leadership training
  • Hands-on experience with real business challenges
  • Access to mentors and senior leadership
  • Clear career progression and performance evaluations

Why Might You Want to Do One?

Explore Different Career Paths

One of the most compelling reasons to join a rotational program is the opportunity to explore several areas within a company before committing to a specific role or career track. This is ideal for individuals who are multi-interested or unsure of what they want to specialize in early in their careers.

Accelerated Career Growth

These programs are often designed to fast-track participants into management roles. Through structured learning and early responsibility, participants can move more quickly into positions of influence compared to traditional entry-level pathways.

Develop a Broad Skill Set

Rotating across functions allows participants to understand how different parts of the organization operate and how they interconnect. This cross-functional exposure is critical for strategic thinking and future leadership.

Build a Strong Professional Network

Participants in LDPs often become part of a cohort, creating lasting professional connections with peers. In addition, these programs typically include access to mentors, exposure to executives, and networking events that can be beneficial for long-term career development.

Gain Global or Strategic Experience

Many multinational companies offer global rotations or placements in high-priority strategic projects. This can enhance your understanding of international business and position you well for global leadership roles.

Examples of Companies Offering These Programs

  • General Electric: Early Career Leadership Programs in Finance, Digital Technology, and Engineering
  • PepsiCo: Leadership Development Programs in Marketing, Sales, and Supply Chain
  • Deloitte: Rotational Analyst Programs in Consulting and Strategy
  • Google: Associate Product Manager (APM) Program
  • Unilever: Future Leaders Program (UFLP) across functions such as Finance, Marketing, and Supply Chain

Who Should Consider These Programs?

Leadership Development and Rotational Programs are well-suited for individuals who:

  • Are curious and open to learning across disciplines
  • Thrive in dynamic, fast-paced environments
  • Aspire to become leaders or take on strategic roles
  • Want a structured yet flexible career path with long-term growth opportunities

Conclusion

Leadership Development and Rotational Programs provide an excellent platform for launching a successful career. They combine hands-on experience with strategic learning opportunities, enabling participants to grow professionally while gaining a comprehensive understanding of business operations. For those seeking a well-supported, high-impact start to their careers, these programs are worth serious consideration.

Economics and Business Analytics double major and a Data Science minor at Lawrence University as well as an Event Coordinator at Pan Asian Organization and Social Media Manager at LU Data Science Club. Connect with Nadika in LinkedIn

Top 5 Entry Level Data Science Roles for Graduates

With the growth of big data and artificial intelligence, data science has become one of the most popular professions. Understanding the various entry-level professions can help fresh graduates break into the sector and begin a successful career. Whether you have a background in statistics, computer science, business analytics, or engineering, there is a position that matches your abilities and interests.

Here are the top five entry-level data science positions that recent graduates should explore.

1. Data Analyst

A Data Analyst collects, processes, and interprets data to help businesses make informed decisions. They focus on identifying trends, creating visualizations, and generating reports.

Key Skills Needed

  • SQL for data extraction
  • Excel for data manipulation
  • Python or R for analysis
  • Data visualization tools (Tableau, Power BI)

2. Machine Learning Engineer (Entry-Level ML Engineer)

A Machine Learning Engineer builds and deploys machine learning models that help automate tasks and improve decision-making. Unlike data analysts, ML engineers focus on model development and optimization.

Key Skills Needed

  • Strong programming skills in Python (NumPy, Pandas, TensorFlow, PyTorch)
  • Understanding of machine learning algorithms
  • Experience with cloud computing (AWS, GCP, Azure)
  • Knowledge of data structures and software engineering principles

3. Data Scientist (Junior/Associate Level)

A Junior Data Scientist analyzes complex datasets, builds predictive models, and provides insights that drive business decisions. They focus on statistics, machine learning, and experimental design.

Key Skills Needed

  • Statistical modeling and hypothesis testing
  • Python/R for data science (Scikit-learn, Statsmodels)
  • SQL for querying large datasets
  • Data visualization (Matplotlib, Seaborn)

4. Business Intelligence (BI) Analyst

A BI Analyst helps businesses optimize performance by analyzing key metrics and creating dashboards. They focus on high-level reporting rather than deep machine learning.

Key Skills Needed

  • SQL for database management
  • Dashboard creation (Power BI, Tableau, Looker)
  • Data storytelling and reporting
  • Business acumen and domain knowledge

5. Data Engineer (Entry-Level)

A Data Engineer focuses on building and maintaining data pipelines, ensuring data is available for analysts and scientists. They work on ETL (Extract, Transform, Load) processes and database management.

Key Skills Needed

  • Strong SQL and database management
  • Big Data frameworks (Hadoop, Spark)
  • Python (Pandas, PySpark)
  • Cloud platforms (AWS, GCP, Azure)

Breaking into data science can be overwhelming, but knowing your strengths and choosing the right entry-level role can make the process easier. Whether you start as a Data Analyst or jump straight into Machine Learning Engineering, continuous learning and hands-on projects will help you advance in your career.

Data Analytics in Sports

In today’s sports world, data analytics has become a game-changer. Whether it’s optimizing player performance, refining team strategies, or enhancing fan engagement, data-driven decision-making is reshaping how sports are played and managed. From professional leagues like the NBA, NFL, and EPL to amateur and college-level teams, data analytics is giving teams a competitive edge like never before.

How Data Analytics is Used in Sports:

1. Performance Analysis and Player Development

Coaches and sports analysts now rely on data to track player performance metrics such as speed, endurance, accuracy, and reaction time. Wearable technology like GPS trackers, heart rate monitors, and motion sensors provide real-time data, helping coaches fine-tune training programs.

Example: The English Premier League’s teams use GPS trackers to monitor player movement and workload, preventing injuries and optimizing performance.

2. Team Strategy and Game Tactics

Data analytics helps coaches understand opponents’ weaknesses and create optimal strategies. By analyzing historical data, teams can predict game patterns, assess player matchups, and adjust tactics accordingly.

Example: In the NBA, teams like the Golden State Warriors use shot analytics to determine high-percentage shooting zones, influencing their three-point-heavy offensive strategy.

3. Fan Engagement and Sports Business Analytics

Data isn’t just for teams—it enhances the fan experience too. From personalized content recommendations to dynamic ticket pricing, sports organizations use data analytics to improve engagement and revenue generation.

Example: The NFL uses fan behavior analytics to enhance marketing strategies, boosting merchandise sales and increasing viewership.

Key Technologies Driving Sports Analytics:

  • Artificial Intelligence & Machine Learning: Predictive modeling for player performance and injury risk.
  • Wearable Technology: Tracks real-time physiological data for training optimization.
  • Video Analysis Software: Breaks down game footage for tactical analysis.
  • Big Data & Cloud Computing: Stores and processes vast amounts of sports data.

Data analytics is revolutionizing sports, providing deeper insights, optimizing performance, and transforming how teams strategize. As technology advances, data-driven decision-making will become an even bigger part of the game.

Building Soft Skills in Tech

Technical expertise is unquestionably necessary in today’s fast changing technological scene. However, as the business increasingly values collaborative problem-solving, innovation, and leadership, soft skills have evolved as an equally crucial component of professional success. Building soft skills in technology is no longer an option—it is required.

Soft skills are personal characteristics that allow you to interact effectively and harmoniously with others. For Tech professionals, mastering these skills improves not only individual performance but also team success and organizational growth. 

Why Soft Skills Matter in Tech

The stereotype of a tech professional working in isolation is far from reality. Today, tech roles require constant collaboration across teams, communication with non-technical stakeholders, and adaptability to ever-changing technologies. Here’s why soft skills matter:

  1. Bridging the Gap Between Technical and Non-Technical Teams
  2. Driving Teamwork and Collaboration
  3. Enhancing Problem-Solving and Creativity
  4. Leadership in Tech
  5. Career Growth and Longevity

Top Soft Skills for Tech Professionals

If you’re looking to thrive in your tech career, focus on cultivating the following soft skills:

  1. Communication Skills
    • Verbal: Explain technical concepts in simple terms.
    • Written: Write clear documentation, emails, and reports.
    • Active Listening: Understand the needs and perspectives of others.
  2. Teamwork and Collaboration
    • Collaborate across diverse teams with mutual respect.
    • Contribute constructively during team discussions.
    • Celebrate team successes and learn from failures.
  3. Adaptability
    • Stay flexible in the face of changing priorities or technologies.
    • Embrace lifelong learning to keep pace with innovation.
  4. Time Management
    • Prioritize tasks effectively to meet deadlines.
    • Balance multiple projects without compromising quality.
    • Set realistic goals and avoid burnout.
  5. Conflict Resolution
    • Approach disagreements professionally and constructively.
    • Mediate disputes to find win-win solutions.
    • Build trust to foster healthy working relationships.
  6. Leadership and Mentoring
    • Inspire and guide team members toward shared goals.
    • Provide constructive feedback and recognize achievements.
    • Advocate for team growth and development.

How to Develop Soft Skills in Tech

Building soft skills takes time and consistent effort. Here are some strategies to accelerate your growth:

1. Practice Active Communication

  • Join meetings prepared to share your thoughts clearly and concisely.
  • Use analogies or stories to explain technical concepts.
  • Seek feedback on your communication style and refine it over time.

2. Engage in Team-Based Projects

  • Volunteer for group assignments or hackathons.
  • Focus on team dynamics rather than individual contributions.
  • Reflect on your role in the team and areas for improvement.

3. Take Courses or Workshops

  • Enroll in training sessions focused on leadership, communication, or emotional intelligence.
  • Explore online platforms like LinkedIn Learning, Coursera, or Udemy for soft skills training.

4. Seek Mentorship

  • Connect with mentors who exhibit strong soft skills.
  • Observe and emulate their communication, leadership, and problem-solving approaches.

5. Step Out of Your Comfort Zone

  • Volunteer to lead presentations or meetings.
  • Take part in networking events to practice interpersonal skills.

Insights from Santosh Kutty: From Lawrence to Leadership at Deloitte

Santosh Kutty’s journey from being an international student at Lawrence University to a Partner at Deloitte exemplifies how a liberal arts education, strategic career moves, and adaptability can lead to remarkable professional success. Here are the key takeaways from his reflections on his journey.

As an international student, Santosh initially faced challenges adjusting to a new environment but soon thrived. Lawrence’s liberal arts education played a pivotal role in his development. By designing his own major that blended computer science, communication, and teamwork skills, he gained both technical expertise and the soft skills critical for professional growth. This unique combination of analytical thinking and interpersonal abilities has been instrumental throughout his career.

During Santosh’s time at Lawrence, computer science wasn’t a fully established major. Undeterred, he collaborated with professors to curate a personalized course load tailored to his long-term goals in technology. His initiative in designing a CS-centric curriculum reflects the

As Vice President of Lawrence International Club, Santosh developed leadership and networking skills that would prove invaluable in his career. This role taught him the importance of building connections and fostering inclusive communities—lessons he carried into his professional life.

Santosh’s path to Deloitte was shaped by his rich educational background and professional experiences. After completing a master’s in computer science from the University of Wisconsin-Milwaukee and an MBA from Northwestern University, he amassed a decade of experience before applying to Deloitte. A key moment in his journey was leveraging his Lawrence connections; a friend from his college days recommended him for a position at Deloitte, showcasing the enduring value of networking.

As a Partner and the US L&A/Group Insurance Technology Practice Leader at Deloitte, Santosh has spearheaded groundbreaking projects in insurance technology. His leadership highlights include:

  • Developing Deloitte’s LifeTech 3S Modernization Method to help insurance carriers drive growth and efficiency through agile technology investments.
  • Leading large-scale transformation projects for global clients, including implementing systems like PEGA, SAPIENS, and FINEOS across multiple countries.
  • Collaborating with diverse teams across geographies to deliver tailored solutions for complex challenges in the insurance and financial sectors.

Santosh’s passion for his role at Deloitte stems from its dynamic nature. No two projects are the same, and interacting with diverse clients keeps the work engaging and fulfilling. For Santosh, the constant evolution of challenges and opportunities makes every day exciting.

Santosh emphasized that finance and insurance companies value adaptability, communication, and networking skills in addition to technical expertise. For undergraduates, particularly international students, networking is crucial. Santosh advises actively attending events, building genuine relationships, and maintaining connections over time. These efforts can open doors to unexpected opportunities, as they did for him.