This race condition occurs because multiple threads can simultaneously read, write, or delete entries from `self.jobs` and `self.results` without any coordination.

To fix this, we introduce a `threading.Lock` object and use the `with` statement (context manager) to ensure that only one thread can execute the critical sections (where shared data is accessed or modified) at any given time.

Here is the complete fixed implementation:

```python
import threading
import time
import random

class JobQueue:
    """
    A thread-safe implementation of a job queue using a threading.Lock.
    """
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Initialize the lock to synchronize access to shared resources
        self._lock = threading.Lock()

    def add_job(self, job_id, data):
        """Adds a job to the queue in a thread-safe manner."""
        with self._lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        """
        Retrieves, processes, and stores the result for a job in a thread-safe manner.
        The entire sequence (check, read, write, delete) must be atomic.
        """
        data = None
        
        # 1. Atomically retrieve and remove the job data
        with self._lock:
            if job_id not in self.jobs:
                return False
            
            # Use pop() to retrieve and remove the item in one atomic operation
            data = self.jobs.pop(job_id, None) 
        
        if data is None:
            # Should only happen if another thread processed it between checks
            return False

        # 2. Execute the long-running processor (outside the lock to prevent blocking other threads)
        # NOTE: If the processor function itself modifies shared state, it must handle its own locking.
        try:
            result = processor(data)
        except Exception as e:
            print(f"Error processing job {job_id}: {e}")
            return False
        
        # 3. Atomically store the result
        with self._lock:
            self.results[job_id] = result
            
        return True

    def get_result(self, job_id):
        """Retrieves a result in a thread-safe manner."""
        with self._lock:
            return self.results.get(job_id)

# --- Example Usage and Verification ---

def simple_processor(data):
    """A simulated job processor."""
    # Simulate work being done
    time.sleep(random.uniform(0.01, 0.1)) 
    return f"Processed data: {data * 2}"

def worker(queue, job_id):
    """A thread worker that attempts to process a specific job."""
    print(f"Worker {job_id} attempting to process job...")
    success = queue.process_job(job_id, simple_processor)
    if success:
        result = queue.get_result(job_id)
        print(f"Worker {job_id} successfully finished job. Result: {result}")
    else:
        print(f"Worker {job_id} failed to process job (already gone or nonexistent).")


if __name__ == '__main__':
    queue = JobQueue()
    
    # Add jobs
    job_ids = [queue.add_job(f'job_{i}', f'input_{i}') for i in range(5)]
    print(f"Jobs added: {job_ids}")

    threads = []
    
    # Create multiple threads to race for the jobs
    # We will create 7 threads, but only 5 jobs exist, forcing contention.
    for i, job_id in enumerate(job_ids):
        t = threading.Thread(target=worker, args=(queue, job_id))
        threads.append(t)
        t.start()
    
    # Start a few extra threads that will fail gracefully
    for i in range(5, 7):
        t = threading.Thread(target=worker, args=(queue, f'job_{i}'))
        threads.append(t)
        t.start()


    # Wait for all threads to complete
    for t in threads:
        t.join()

    print("\n--- Final State Check ---")
    print(f"Jobs remaining in queue: {queue.jobs}")
    print(f"Results available: {len(queue.results)}")
    
    # Verify all 5 initial jobs were processed exactly once
    assert len(queue.results) == 5
    assert len(queue.jobs) == 0
    print("Verification successful: All jobs were processed exactly once.")
```

### Explanation of the Fixes

1.  **`self._lock = threading.Lock()`:** A lock object is initialized in `__init__`. This object acts like a gatekeeper, ensuring only one thread can pass through a protected section at a time.
2.  **`with self._lock:`:** We use the Python context manager (`with`) around every critical section. When a thread enters a `with self._lock:` block, it attempts to acquire the lock. If the lock is already held by another thread, the current thread blocks until the lock is released. When the `with` block exits, the lock is automatically released.
3.  **`process_job` Optimization:** The most critical fix was in `process_job`. Instead of reading the data and then deleting it (which is two separate, unprotected operations), we now use `self.jobs.pop(job_id, None)` inside the lock. This atomically retrieves and removes the job data, guaranteeing that no other thread can read or delete that job simultaneously.
4.  **Separating Processing:** We moved the actual `processor(data)` call *outside* of the lock. If the processing takes a long time, holding the lock during that time would serialize the entire queue (meaning only one job could be processed at a time). By releasing the lock before processing, we allow other threads to add jobs or retrieve results while one job is running. We only re-acquire the lock for the brief moment needed to write the final result.